Expanded genetic landscape of chronic obstructive ...



Expanded genetic landscape of chronic obstructive pulmonary disease reveals heterogeneous cell type and phenotype associationsSummaryBACKGROUND: Chronic obstructive pulmonary disease (COPD) risk is influenced by genetics, but the majority of genetic factors are still unknown. The increasing availability of large-scale datasets for discovery and interpretation of genetic loci can provide novel insights into disease pathogenesis, including cell type and phenotype associations.METHODS: We performed a genome-wide association study of spirometrically-defined COPD in 24 studies, including the UK Biobank. We sought to determine, through bioinformatic and computational analysis, the likely set of variants, genes, cell types, and biologic pathways implicated by these associations. Finally, we assessed our genetic findings for relevance to COPD-related phenotypes, other respiratory diseases, and comorbidities.FINDINGS: In a total of 35,164 cases (21,081 from the Biobank) and 215,903 controls, we identified 82 loci with P value < 5 x 10-8. Of 35 signals not previously described in association with COPD or lung function, 13 replicated through association with lung function measures in the SpiroMeta consortium, most of the remaining signals had suggestive levels of association. Using gene expression and regulation data, we identified enrichment of signal in fetal lung, smooth muscle and alveolar type II cells. Candidate effector genes and pathways implicated by bioinformatic analysis included CHIA, IL17RD, OBFC1, and ITGB8. We found 15 shared genomic regions between COPD and asthma and 4 regions overlapping between COPD and pulmonary fibrosis. COPD genetic risk loci clustered into subsets with heterogeneous quantitative imaging features and comorbidity associations.INTERPRETATION: In a large-scale genetic associations study of COPD, genetic loci implicate a veried set of cell types, disease pathways, and phenotypes. Our study substantially expands the number of COPD-associated loci and provides multiple avenues for investigation of COPD pathogenesis and disease heterogeneity.FUNDING: US National Heart, Lung, and Blood Institute; the Alpha-1 Foundation; the COPD Foundation through contributions from AstraZeneca, Boehringer Ingelheim, Novartis, and Sepracor; GlaxoSmithKline; Centers for Medicare and Medicaid Services; Agency for Healthcare Research and Quality; and US Department of Veterans Affairs.BackgroundChronic obstructive pulmonary disease (COPD) is a disease of enormous and growing global burdenADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S2213-2600(17)30293-X","ISSN":"2213-2619","PMID":"28822787","abstract":"BACKGROUND Chronic obstructive pulmonary disease (COPD) and asthma are common diseases with a heterogeneous distribution worldwide. Here, we present methods and disease and risk estimates for COPD and asthma from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) 2015 study. The GBD study provides annual updates on estimates of deaths, prevalence, and disability-adjusted life years (DALYs), a summary measure of fatal and non-fatal disease outcomes, for over 300 diseases and injuries, for 188 countries from 1990 to the most recent year. METHODS We estimated numbers of deaths due to COPD and asthma using the GBD Cause of Death Ensemble modelling (CODEm) tool. First, we analysed data from vital registration and verbal autopsy for the aggregate category of all chronic respiratory diseases. Subsequently, models were run for asthma and COPD relying on covariates to predict rates in countries that have incomplete or no vital registration data. Disease estimates for COPD and asthma were based on systematic reviews of published papers, unpublished reports, surveys, and health service encounter data from the USA. We used the Global Initiative of Chronic Obstructive Lung Disease spirometry-based definition as the reference for COPD and a reported diagnosis of asthma with current wheeze as the definition of asthma. We used a Bayesian meta-regression tool, DisMod-MR 2.1, to derive estimates of prevalence and incidence. We estimated population-attributable fractions for risk factors for COPD and asthma from exposure data, relative risks, and a theoretical minimum exposure level. Results were stratified by Socio-demographic Index (SDI), a composite measure of income per capita, mean years of education over the age of 15 years, and total fertility rate. FINDINGS In 2015, 3·2 million people (95% uncertainty interval [UI] 3·1 million to 3·3 million) died from COPD worldwide, an increase of 11·6% (95% UI 5·3 to 19·8) compared with 1990. There was a decrease in age-standardised death rate of 41·9% (37·7 to 45·1) but this was counteracted by population growth and ageing of the global population. From 1990 to 2015, the prevalence of COPD increased by 44·2% (41·7 to 46·6), whereas age-standardised prevalence decreased by 14·7% (13·5 to 15·9). In 2015, 0·40 million people (0·36 million to 0·44 million) died from asthma, a decrease of 26·7% (-7·2 to 43·7) from 1990, and the age-standardised death rate decreased by 58·8% (39·0 to 69·0). The prevalence of asthma incr…","author":[{"dropping-particle":"","family":"GBD 2015 Chronic Respiratory Disease Collaborators","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet. Respiratory medicine","id":"ITEM-1","issue":"9","issued":{"date-parts":[["2017"]]},"page":"691-706","title":"Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.","type":"article-journal","volume":"5"},"uris":[""]}],"mendeley":{"formattedCitation":"¹","plainTextFormattedCitation":"1","previouslyFormattedCitation":"¹"},"properties":{"noteIndex":0},"schema":""}1. Environmental risk factors, predominately cigarette smoking, account for a large fraction of disease risk, but there is considerable variability in COPD susceptibility among individuals with similar smoking exposure. Studies in families and in populations demonstrate that genetic factors account for a substantial fraction of disease susceptibility. The most well-known genetic risk factor for COPD, alpha-1 antitrypsin deficiency, is due to rare variants in the SERPINA1 gene, and has been well-known for over half a century. However, while a few additional rare variants have been discovered in COPD, it is likely that, similar to other adult onset complex diseases, the majority of population risk lies in common variantsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nature18642","ISSN":"1476-4687","PMID":"27398621","abstract":"The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.","author":[{"dropping-particle":"","family":"Fuchsberger","given":"Christian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Flannick","given":"Jason","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Teslovich","given":"Tanya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mahajan","given":"Anubha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Agarwala","given":"Vineeta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gaulton","given":"Kyle 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These variants, while individually of weak effect, can lead to novel insights into disease pathogenesisADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1164/rccm.201505-0999OC","ISBN":"1535-4970 (Electronic)\r1073-449X (Linking)","PMID":"26862784","abstract":"RATIONALE: A genetic locus within the FAM13A gene has been consistently associated with chronic obstructive pulmonary disease (COPD) in genome-wide association studies. However, the mechanisms by which FAM13A contributes to COPD susceptibility are unknown. OBJECTIVES: To determine the biologic function of FAM13A in human COPD and murine COPD models and discover the molecular mechanism by which FAM13A influences COPD susceptibility. METHODS: Fam13a null mice (Fam13a(-/-)) were generated and exposed to cigarette smoke. The lung inflammatory response and airspace size were assessed in Fam13a(-/-) and Fam13a(+/+) littermate control mice. Cellular localization of FAM13A protein and mRNA levels of FAM13A in COPD lungs were assessed using immunofluorescence, Western blotting, and reverse transcriptase-polymerase chain reaction, respectively. Immunoprecipitation followed by mass spectrometry identified cellular proteins that interact with FAM13A to reveal insights on FAM13A's function. MEASUREMENTS AND MAIN RESULTS: In murine and human lungs, FAM13A is expressed in airway and alveolar type II epithelial cells and macrophages. Fam13a null mice (Fam13a(-/-)) were resistant to chronic cigarette smoke-induced emphysema compared with Fam13a(+/+) mice. In vitro, FAM13A interacts with protein phosphatase 2A and recruits protein phosphatase 2A with glycogen synthase kinase 3beta and beta-catenin, inducing beta-catenin degradation. Fam13a(-/-) mice were also resistant to elastase-induced emphysema, and this resistance was reversed by coadministration of a beta-catenin inhibitor, suggesting that FAM13A could increase the susceptibility of mice to emphysema development by inhibiting beta-catenin signaling. Moreover, human COPD lungs had decreased protein levels of beta-catenin and increased protein levels of FAM13A. 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However, in vivo evidence connecting Hhip to age-related FEV1 decline and emphysema development is lacking. Herein, using Hhip heterozygous mice (Hhip(+/-)), we observed increased lung compliance and spontaneous emphysema in Hhip(+/-) mice starting at 10 mo of age. This increase was preceded by increases in oxidative stress levels in the lungs of Hhip(+/-) vs. Hhip(+/+) mice. To our knowledge, these results provide the first line of evidence that HHIP is involved in maintaining normal lung function and alveolar structures. Interestingly, antioxidant N-acetyl cysteine treatment in mice starting at age of 5 mo improved lung function and prevented emphysema development in Hhip(+/-) mice, suggesting that N-acetyl cysteine treatment limits the progression of age-related emphysema in Hhip(+/-) mice. Therefore, reduced lung function and age-related spontaneous emphysema development in Hhip(+/-) mice may be caused by increased oxidative stress levels in murine lungs as a result of haploinsufficiency of Hhip.","author":[{"dropping-particle":"","family":"Lao","given":"Taotao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jiang","given":"Zhiqiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yun","given":"Jeong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qiu","given":"Weiliang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guo","given":"Feng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Chunfang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mancini","given":"John Dominic","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gupta","given":"Kushagra","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laucho-Contreras","given":"Maria E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Naing","given":"Zun Zar Chi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Li","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perrella","given":"Mark A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Owen","given":"Caroline A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"Edwin K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Xiaobo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedings of the National Academy of Sciences of the United States of America","id":"ITEM-2","issue":"32","issued":{"date-parts":[["2016"]]},"page":"E4681-7","title":"Hhip haploinsufficiency sensitizes mice to age-related emphysema.","type":"article-journal","volume":"113"},"uris":[""]}],"mendeley":{"formattedCitation":"^3,4","plainTextFormattedCitation":"3,4","previouslyFormattedCitation":"^3,4"},"properties":{"noteIndex":0},"schema":""}3,4. The discovery of each additional locus is not only in itself an opportunity to discovery potentially new biology, but can lead to more global insights, such as functional or disease links between loci, or identification of which cell types may be more likely to drive the genetics of COPD risk. Recently, we performed a large genome-wide association analysis of COPD as part of the International COPD Genetics Consortium, identifying 22 genome-wide significant lociADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3752","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","ISSN":"1546-1718","PMID":"28166215","abstract":"Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. We performed a genetic association study in 15,256 cases and 47,936 controls, with replication of select top results (P < 5 x 10-6) in 9,498 cases and 9,748 controls. In the combined meta-analysis, we identified 22 loci associated at genome-wide significance, including 13 new associations with COPD. Nine of these 13 loci have been associated with lung function in general population samples, while 4 (EEFSEC, DSP, MTCL1, and SFTPD) are new. We noted two loci shared with pulmonary fibrosis (FAM13A and DSP) but that had opposite risk alleles for COPD. None of our loci overlapped with genome-wide associations for asthma, although one locus has been implicated in joint susceptibility to asthma and obesity. We also identified genetic correlation between COPD and asthma. 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Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; COPDGene Investigators; ECLIPSE Investigators; LifeLines Investigators; SPIROMICS Research Group; International COPD Genetics Network Investigators; UK BiLEVE Investigators; International COPD Genetics Consortium; Bosse, Y; Shrine, Nick; Artigas, María Soler; Wain, Louise V; Hall, Ian P; Jackson, Victoria E; Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Pare, P D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Consortium Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium Network Investigators)\n\nFrom Duplicate 1 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.\n\nFrom Duplicate 2 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. 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We sought to combine this data with the UK BiobankADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pmed.1001779","ISSN":"1549-1676","PMID":"25826379","abstract":"Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.","author":[{"dropping-particle":"","family":"Sudlow","given":"Cathie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gallacher","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Allen","given":"Naomi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beral","given":"Valerie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burton","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Danesh","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Downey","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elliott","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Green","given":"Jane","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Landray","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Bette","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Matthews","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ong","given":"Giok","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pell","given":"Jill","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silman","given":"Alan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Young","given":"Alan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sprosen","given":"Tim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Peakman","given":"Tim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Collins","given":"Rory","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS medicine","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2015","3"]]},"page":"e1001779","title":"UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.","type":"article-journal","volume":"12"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶","plainTextFormattedCitation":"6","previouslyFormattedCitation":"⁶"},"properties":{"noteIndex":0},"schema":""}6, a population-based study of several hundred thousand subjects with lung function and cigarette smoking assessment. We hypothesized that this larger genome-wide association study would confirm prior associations with COPD, show additional overlap with known associations with population-based lung function, and also identify new COPD susceptibility loci. We sought to determine, through bioinformatic and computational analysis, the likely set of variants, genes, and cell types, and biologic pathways, implicated by these associations. Finally, we assessed our genetic findings for relevance to COPD-specific, respiratory, and other phenotypes.MethodsStudy populationsThe UK Biobank is a population-based cohort consisting of 502,682 individualsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pmed.1001779","ISSN":"1549-1676","PMID":"25826379","abstract":"Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.","author":[{"dropping-particle":"","family":"Sudlow","given":"Cathie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gallacher","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Allen","given":"Naomi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beral","given":"Valerie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burton","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Danesh","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Downey","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elliott","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Green","given":"Jane","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Landray","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Bette","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Matthews","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ong","given":"Giok","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pell","given":"Jill","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silman","given":"Alan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Young","given":"Alan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sprosen","given":"Tim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Peakman","given":"Tim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Collins","given":"Rory","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS medicine","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2015","3"]]},"page":"e1001779","title":"UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.","type":"article-journal","volume":"12"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶","plainTextFormattedCitation":"6","previouslyFormattedCitation":"⁶"},"properties":{"noteIndex":0},"schema":""}6. To determine lung function, we used measures of forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC) derived from the blow curve time series and subject to additional quality control based on ATS/ERS criteriaADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1183/09031936.05.00034805","ISSN":"0903-1936","PMID":"16055882","author":[{"dropping-particle":"","family":"Miller","given":"M R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hankinson","given":"J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brusasco","given":"V","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burgos","given":"F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Casaburi","given":"R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Coates","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crapo","given":"R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Enright","given":"P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grinten","given":"C P M","non-dropping-particle":"van der","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gustafsson","given":"P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jensen","given":"R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"D C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"MacIntyre","given":"N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McKay","given":"R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Navajas","given":"D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pedersen","given":"O F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pellegrino","given":"R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Viegi","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wanger","given":"J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"ATS/ERS Task Force","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The European respiratory journal","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2005","8"]]},"page":"319-38","title":"Standardisation of spirometry.","type":"article-journal","volume":"26"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷","plainTextFormattedCitation":"7","previouslyFormattedCitation":"⁷"},"properties":{"noteIndex":0},"schema":""}7 (Supplementary Text). We defined COPD in white subjects using two pre-bronchodilator measurements of lung function according to modified GOLD criteria for moderate to very severe airflow limitationADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1164/rccm.201701-0218PP","ISBN":"1535-4970 (Electronic)\r1073-449X (Linking)","ISSN":"1535-4970","PMID":"28128970","abstract":"This Executive Summary of the Global Strategy for the Diagnosis, Management, and Prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017 report focuses primarily on the revised and novel parts of the document. The most significant changes include: (1) the assessment of chronic obstructive pulmonary disease has been refined to separate the spirometric assessment from symptom evaluation. ABCD groups are now proposed to be derived exclusively from patient symptoms and their history of exacerbations; (2) for each of the groups A to D, escalation strategies for pharmacologic treatments are proposed; (3) the concept of deescalation of therapy is introduced in the treatment assessment scheme; (4) nonpharmacologic therapies are comprehensively presented; and (5) the importance of comorbid conditions in managing chronic obstructive pulmonary disease is reviewed.","author":[{"dropping-particle":"","family":"Vogelmeier","given":"Claus F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Criner","given":"Gerard J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martinez","given":"Fernando J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anzueto","given":"Antonio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barnes","given":"Peter J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bourbeau","given":"Jean","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Celli","given":"Bartolome R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Rongchang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Decramer","given":"Marc","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fabbri","given":"Leonardo M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frith","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Halpin","given":"David M G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"López Varela","given":"M Victorina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nishimura","given":"Masaharu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Roche","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rodriguez-Roisin","given":"Roberto","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sin","given":"Don D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Singh","given":"Dave","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stockley","given":"Robert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vestbo","given":"J?rgen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wedzicha","given":"Jadwiga A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Agustí","given":"Alvar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V","family":"Lopez Varela","given":"M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nishimura","given":"Masaharu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Roche","given":"Nicolas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rodriguez-Roisin","given":"Roberto","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sin","given":"Don D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Singh","given":"Dave","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stockley","given":"Robert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vestbo","given":"J?rgen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wedzicha","given":"Jadwiga A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Agusti","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of respiratory and critical care medicine","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2017","3","1"]]},"note":"From Duplicate 1 (Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary - Vogelmeier, C F; Criner, G J; Martinez, F J; Anzueto, A; Barnes, P J; Bourbeau, J; Celli, B R; Chen, R; Decramer, M; Fabbri, L M; Frith, P; Halpin, D M; Lopez Varela, M V; Nishimura, M; Roche, N; Rodriguez-Roisin, R; Sin, D D; Singh, D; Stockley, R; Vestbo, J; Wedzicha, J A; Agusti, A)\n\nVogelmeier, Claus F\nCriner, Gerard J\nMartinez, Fernando J\nAnzueto, Antonio\nBarnes, Peter J\nBourbeau, Jean\nCelli, Bartolome R\nChen, Rongchang\nDecramer, Marc\nFabbri, Leonardo M\nFrith, Peter\nHalpin, David M G\nLopez Varela, M Victorina\nNishimura, Masaharu\nRoche, Nicolas\nRodriguez-Roisin, Roberto\nSin, Don D\nSingh, Dave\nStockley, Robert\nVestbo, Jorgen\nWedzicha, Jadwiga A\nAgusti, Alvar\neng\nG0800570/Medical Research Council/United Kingdom\nG1001365/Medical Research Council/United Kingdom\nG1001372/Medical Research Council/United Kingdom\n2017/01/28 06:00\nAm J Respir Crit Care Med. 2017 Mar 1;195(5):557-582. doi: 10.1164/rccm.201701-0218PP.\n\nFrom Duplicate 2 (Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary - Vogelmeier, Claus F; Criner, Gerard J; Martinez, Fernando J; Anzueto, Antonio; Barnes, Peter J; Bourbeau, Jean; Celli, Bartolome R; Chen, Rongchang; Decramer, Marc; Fabbri, Leonardo M; Frith, Peter; Halpin, David M G; López Varela, M Victorina; Nishimura, Masaharu; Roche, Nicolas; Rodriguez-Roisin, Roberto; Sin, Don D; Singh, Dave; Stockley, Robert; Vestbo, J?rgen; Wedzicha, Jadwiga A; Agustí, Alvar; Lopez Varela, M V; Nishimura, Masaharu; Roche, Nicolas; Rodriguez-Roisin, Roberto; Sin, Don D; Singh, Dave; Stockley, Robert; Vestbo, J?rgen; Wedzicha, Jadwiga A; Agusti, A)\n\nFrom Duplicate 1 (Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary - Vogelmeier, C F; Criner, G J; Martinez, F J; Anzueto, A; Barnes, P J; Bourbeau, J; Celli, B R; Chen, R; Decramer, M; Fabbri, L M; Frith, P; Halpin, D M; Lopez Varela, M V; Nishimura, M; Roche, N; Rodriguez-Roisin, R; Sin, D D; Singh, D; Stockley, R; Vestbo, J; Wedzicha, J A; Agusti, A)\n\nVogelmeier, Claus F\nCriner, Gerard J\nMartinez, Fernando J\nAnzueto, Antonio\nBarnes, Peter J\nBourbeau, Jean\nCelli, Bartolome R\nChen, Rongchang\nDecramer, Marc\nFabbri, Leonardo M\nFrith, Peter\nHalpin, David M G\nLopez Varela, M Victorina\nNishimura, Masaharu\nRoche, Nicolas\nRodriguez-Roisin, Roberto\nSin, Don D\nSingh, Dave\nStockley, Robert\nVestbo, Jorgen\nWedzicha, Jadwiga A\nAgusti, Alvar\neng\nG0800570/Medical Research Council/United Kingdom\nG1001365/Medical Research Council/United Kingdom\nG1001372/Medical Research Council/United Kingdom\n2017/01/28 06:00\nAm J Respir Crit Care Med. 2017 Mar 1;195(5):557-582. doi: 10.1164/rccm.201701-0218PP.","page":"557-582","title":"Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. 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Caucasian subjects had higher mean FVC and FEV1 values than did Mexican-American and African-American subjects across the entire age range. However, Caucasian and Mexican-American subjects had similar FVC and FEV1 values with respect to height, and African-American subjects had lower values. These differences may be partially due to differences in body build: observed Mexican-Americans were shorter than Caucasian subjects of the same age, and African-Americans on average have a smaller trunk:leg ratio than do Caucasians. Reference values and lower limits of normal were derived using a piecewise polynomial model with age and height as predictors. These reference values encompass a wide age range for three race/ethnic groups and should prove useful for diagnostic and research purposes.","author":[{"dropping-particle":"","family":"Hankinson","given":"J L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Odencrantz","given":"J R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fedan","given":"K B","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of respiratory and critical care medicine","id":"ITEM-1","issue":"1","issued":{"date-parts":[["1999","1"]]},"page":"179-87","title":"Spirometric reference values from a sample of the general U.S. population.","type":"article-journal","volume":"159"},"uris":[""]}],"mendeley":{"formattedCitation":"⁹","plainTextFormattedCitation":"9","previouslyFormattedCitation":"⁹"},"properties":{"noteIndex":0},"schema":""}9), and the ratio between FEV1 and FVC less than 0.7. Genotyping was performed using Axiom UK BiLEVE array and Axiom Biobank array (Affymetrix, Santa Clara, California, USA) and imputed to the Haplotype Reference Consortium (HRC) version 1.1 panelADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3643","ISSN":"1546-1718","PMID":"27548312","abstract":"We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. 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Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Paré, Peter D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; COPDGene Investigators; ECLIPSE Investigators; LifeLines Investigators; SPIROMICS Research Group; International COPD Genetics Network Investigators; UK BiLEVE Investigators; International COPD Genetics Consortium; Bosse, Y; Shrine, Nick; Artigas, María Soler; Wain, Louise V; Hall, Ian P; Jackson, Victoria E; Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Pare, P D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Consortium Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium Network Investigators)\n\nFrom Duplicate 1 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.\n\nFrom Duplicate 2 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.","page":"426-432","title":"Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis","type":"article-journal","volume":"49"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵","plainTextFormattedCitation":"5","previouslyFormattedCitation":"⁵"},"properties":{"noteIndex":0},"schema":""}5 (Supplementary Text). Briefly, ICGC cohorts performed case-control association analysis based on pre-bronchodilator measurements of FEV1 and FEV1/FVC, and cases were identified using modified GOLD criteria, as above.We performed lookups of select significant variants for FEV1 and FEV1/FVC in the SpiroMeta consortium meta-analysis. Briefly, SpiroMeta comprised of a total of 83,118 individuals from 21 studies imputed to either the 1000 Genomes Project Phase 1 reference panel (13 studies) or the HRC (9 studies). Each study performed linear regression adjusting for age, age2, sex, and height, using rank-based inverse normal transforms, adjusting for population substructure using principal components or linear mixed models, and performing separate analyses for ever- and never- smokers or using a covariate (for studies of related subjects). Genomic control was applied to individual studies, and results were combined using fixed-effects meta-analysis.Genome-wide association analysisIn UK Biobank, we performed logistic regression of COPD, adjusting for age, sex, genotyping array, smoking pack-years, ever smoking status, and principal components of genetic ancestry. Association analysis was done using PLINK 2.0 alphaADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/s13742-015-0047-8","ISSN":"2047-217X","PMID":"25722852","abstract":"BACKGROUND PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. FINDINGS To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, [Formula: see text]-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). CONCLUSIONS The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.","author":[{"dropping-particle":"","family":"Chang","given":"Christopher C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chow","given":"Carson C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tellier","given":"Laurent Cam","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vattikuti","given":"Shashaank","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Purcell","given":"Shaun M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lee","given":"James J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"GigaScience","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"page":"7","title":"Second-generation PLINK: rising to the challenge of larger and richer datasets.","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"¹¹","plainTextFormattedCitation":"11","previouslyFormattedCitation":"¹¹"},"properties":{"noteIndex":0},"schema":""}11 (downloaded on December 11, 2017) with Firth-fallback settings, using Firth regression when quasi-complete separation or regular-logistic-regression convergence failure occurred. We performed a fixed-effects meta-analysis of all ICGC cohorts and UK Biobank using METAL (version 2010-08-01)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btq340","ISBN":"1367-4811 (Electronic)\r1367-4803 (Linking)","PMID":"20616382","abstract":"SUMMARY: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. AVAILABILITY AND IMPLEMENTATION: METAL, including source code, documentation, examples, and executables, is available at .","author":[{"dropping-particle":"","family":"Willer","given":"C J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abecasis","given":"G R","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics","id":"ITEM-1","issue":"17","issued":{"date-parts":[["2010"]]},"note":"Willer, Cristen J\nLi, Yun\nAbecasis, Goncalo R\neng\nR01 CA082659/CA/NCI NIH HHS/\nK99HL094535/HL/NHLBI NIH HHS/\nMH084698/MH/NIMH NIH HHS/\nHG0005214/HG/NHGRI NIH HHS/\nR01 MH084698/MH/NIMH NIH HHS/\nCA082659-11S1/CA/NCI NIH HHS/\nR01 HG002651/HG/NHGRI NIH HHS/\nU01 HG005214/HG/NHGRI NIH HHS/\nK99 HL094535/HL/NHLBI NIH HHS/\nDK078150-03/DK/NIDDK NIH HHS/\nHG0002651/HG/NHGRI NIH HHS/\nR01 DK078150/DK/NIDDK NIH HHS/\nMeta-Analysis\nResearch Support, N.I.H., Extramural\nEngland\n2010/07/10 06:00\nBioinformatics. 2010 Sep 1;26(17):2190-1. doi: 10.1093/bioinformatics/btq340. Epub 2010 Jul 8.","page":"2190-2191","title":"METAL: fast and efficient meta-analysis of genomewide association scans","type":"article-journal","volume":"26"},"uris":[""]}],"mendeley":{"formattedCitation":"¹²","plainTextFormattedCitation":"12","previouslyFormattedCitation":"¹²"},"properties":{"noteIndex":0},"schema":""}12. We assessed population substructure and cryptic relatedness by linkage disequilibrium (LD) score regression interceptADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3211","ISSN":"1546-1718","PMID":"25642630","abstract":"Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.","author":[{"dropping-particle":"","family":"Bulik-Sullivan","given":"Brendan K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loh","given":"Po-Ru","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Finucane","given":"Hilary K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ripke","given":"Stephan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Jian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schizophrenia Working Group of the Psychiatric Genomics Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Patterson","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Daly","given":"Mark J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alkes L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neale","given":"Benjamin M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2015","3"]]},"page":"291-5","title":"LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.","type":"article-journal","volume":"47"},"uris":[""]}],"mendeley":{"formattedCitation":"¹³","plainTextFormattedCitation":"13","previouslyFormattedCitation":"¹³"},"properties":{"noteIndex":0},"schema":""}13. We defined a genetic locus using a 2-Mb window (1 Mb at either end) around a lead variant, with conditional analyses as described below.To maximize our power to identify existing and discover new loci, we examined all loci at the traditional genome-wide significance value of 5 x 10-8. Given the strong genetic correlation between population-based lung function and COPD, we used FEV1 and FEV1/FVC in the SpiroMeta study to provide evidence of replication (Figure 1). We first characterized loci as being previously described (evidence of prior association with lung function or COPD) or novel. For novel loci, we attempted to provide evidence of replication using SpiroMeta, with Bonferroni correction for number of tests. We also examined loci meeting a nominal level of significance, or having consistent direction of effect in SpiroMeta.Cigarette smoking is the major environmental risk factor for COPD. While we adjusted for cigarette smoking in our analysis, we and others have previously identified associations with cigarette smoking both overall and at individual lociADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3752","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","ISSN":"1546-1718","PMID":"28166215","abstract":"Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. We performed a genetic association study in 15,256 cases and 47,936 controls, with replication of select top results (P < 5 x 10-6) in 9,498 cases and 9,748 controls. In the combined meta-analysis, we identified 22 loci associated at genome-wide significance, including 13 new associations with COPD. Nine of these 13 loci have been associated with lung function in general population samples, while 4 (EEFSEC, DSP, MTCL1, and SFTPD) are new. We noted two loci shared with pulmonary fibrosis (FAM13A and DSP) but that had opposite risk alleles for COPD. None of our loci overlapped with genome-wide associations for asthma, although one locus has been implicated in joint susceptibility to asthma and obesity. We also identified genetic correlation between COPD and asthma. Our findings highlight new loci associated with COPD, demonstrate the importance of specific loci associated with lung function to COPD, and identify potential regions of genetic overlap between COPD and other respiratory diseases.","author":[{"dropping-particle":"","family":"Hobbs","given":"Brian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jong","given":"Kim","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lamontagne","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bosse","given":"Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shrine","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Artigas","given":"María 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Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Paré, Peter D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; COPDGene Investigators; ECLIPSE Investigators; LifeLines Investigators; SPIROMICS Research Group; International COPD Genetics Network Investigators; UK BiLEVE Investigators; International COPD Genetics Consortium; Bosse, Y; Shrine, Nick; Artigas, María Soler; Wain, Louise V; Hall, Ian P; Jackson, Victoria E; Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Pare, P D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Consortium Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium Network Investigators)\n\nFrom Duplicate 1 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.\n\nFrom Duplicate 2 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.","page":"426-432","title":"Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis","type":"article-journal","volume":"49"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/ng.571","ISSN":"1546-1718","PMID":"20418890","abstract":"Consistent but indirect evidence has implicated genetic factors in smoking behavior. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], beta = 1.03, standard error (s.e.) = 0.053, P = 2.8 x 10(-73)). Two 10q25 SNPs (rs1329650[G], beta = 0.367, s.e. = 0.059, P = 5.7 x 10(-10); and rs1028936[A], beta = 0.446, s.e. = 0.074, P = 1.3 x 10(-9)) and one 9q13 SNP in EGLN2 (rs3733829[G], beta = 0.333, s.e. = 0.058, P = 1.0 x 10(-8)) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04-1.08, P = 1.8 x 10(-8)). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08-1.18, P = 3.6 x 10(-8)) was significantly associated with smoking cessation.","author":[{"dropping-particle":"","family":"Tobacco and Genetics Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-2","issue":"5","issued":{"date-parts":[["2010","5"]]},"page":"441-7","title":"Genome-wide meta-analyses identify multiple loci associated with smoking behavior.","type":"article-journal","volume":"42"},"uris":[""]}],"mendeley":{"formattedCitation":"^5,14","plainTextFormattedCitation":"5,14","previouslyFormattedCitation":"^5,14"},"properties":{"noteIndex":0},"schema":""}5,14. To further examine these effects, we additionally tested for association of each locus with cigarette smoking and in two separate analyses of ever- and never- smokers in UK Biobank. Identification of independent associations at genome-wide significant lociWe identified specific independent associations at genome-wide significant loci using GCTA-COJOADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.2213","ISSN":"1546-1718","PMID":"22426310","abstract":"We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.","author":[{"dropping-particle":"","family":"Yang","given":"Jian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ferreira","given":"Teresa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morris","given":"Andrew P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Medland","given":"Sarah E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Genetic Investigation of ANthropometric Traits (GIANT) Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Madden","given":"Pamela A F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Heath","given":"Andrew C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Martin","given":"Nicholas G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Montgomery","given":"Grant W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weedon","given":"Michael N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loos","given":"Ruth J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frayling","given":"Timothy M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McCarthy","given":"Mark I","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hirschhorn","given":"Joel N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goddard","given":"Michael E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Visscher","given":"Peter M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2012","3","18"]]},"page":"369-75, S1-3","title":"Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits.","type":"article-journal","volume":"44"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁵","plainTextFormattedCitation":"15","previouslyFormattedCitation":"¹⁵"},"properties":{"noteIndex":0},"schema":""}15. This method utilizes approximate conditional and joint analysis approach which requires summary statistics and representative LD information. We used 10,000 randomly drawn individuals from the UK Biobank discovery dataset as a LD reference sample. We scaled genome-wide significance to a 2-Mb region, resulting in a locus-wide significant threshold of 8 x 10-5, or 2 x 10-6 for variants in MHC locus (chr6:28477797-33448354 in hg19). We created regional association plots via LocusZoom using 1000 Genomes EUR reference data (Nov2014 release)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btq419","ISSN":"1367-4811","PMID":"20634204","abstract":"UNLABELLED Genome-wide association studies (GWAS) have revealed hundreds of loci associated with common human genetic diseases and traits. We have developed a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format. LocusZoom visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns and the positions of genes in the region. AVAILABILITY LocusZoom can be accessed from a web interface at . Users may generate a single plot using a web form, or many plots using batch mode. The software utilizes LD information from HapMap Phase II (CEU, YRI and JPT+CHB) or 1000 Genomes (CEU) and gene information from the UCSC browser, and will accept SNP identifiers in dbSNP or 1000 Genomes format. Single plots are generated in approximately 20 s. Source code and associated databases are available for download and local installation, and full documentation is available online.","author":[{"dropping-particle":"","family":"Pruim","given":"Randall J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Welch","given":"Ryan P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sanna","given":"Serena","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Teslovich","given":"Tanya M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chines","given":"Peter S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gliedt","given":"Terry P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boehnke","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abecasis","given":"Gon?alo R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Willer","given":"Cristen J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics (Oxford, England)","id":"ITEM-1","issue":"18","issued":{"date-parts":[["2010","9","15"]]},"page":"2336-7","title":"LocusZoom: regional visualization of genome-wide association scan results.","type":"article-journal","volume":"26"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁶","plainTextFormattedCitation":"16","previouslyFormattedCitation":"¹⁶"},"properties":{"noteIndex":0},"schema":""}16. Identification and prioritization of tissues and cell types, candidate variants, genes, and pathwaysIdentification of enriched tissues and specific cell typesWe used LD Score Regression (LDSC)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3404","ISSN":"1546-1718","PMID":"26414678","abstract":"Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here we analyze a broad set of functional elements, including cell type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits with an average sample size of 73,599. To enable this analysis, we introduce a new method, stratified LD score regression, for partitioning heritability from GWAS summary statistics while accounting for linked markers. This new method is computationally tractable at very large sample sizes and leverages genome-wide information. Our findings include a large enrichment of heritability in conserved regions across many traits, a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers and many cell type-specific enrichments, including significant enrichment of central nervous system cell types in the heritability of body mass index, age at menarche, educational attainment and smoking behavior.","author":[{"dropping-particle":"","family":"Finucane","given":"Hilary K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bulik-Sullivan","given":"Brendan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gusev","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Trynka","given":"Gosia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reshef","given":"Yakir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loh","given":"Po-Ru","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anttila","given":"Verneri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Han","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zang","given":"Chongzhi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Farh","given":"Kyle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ripke","given":"Stephan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Day","given":"Felix R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"ReproGen Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schizophrenia Working Group of the Psychiatric Genomics Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"RACI Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Purcell","given":"Shaun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stahl","given":"Eli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lindstrom","given":"Sara","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perry","given":"John R B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Okada","given":"Yukinori","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raychaudhuri","given":"Soumya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Daly","given":"Mark J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Patterson","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neale","given":"Benjamin M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alkes L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2015","11"]]},"page":"1228-35","title":"Partitioning heritability by functional annotation using genome-wide association summary statistics.","type":"article-journal","volume":"47"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁷","plainTextFormattedCitation":"17","previouslyFormattedCitation":"¹⁷"},"properties":{"noteIndex":0},"schema":""}17 to estimate the enrichment of functional annotation in disease heritability. We utilized LDSC baseline models (e.g., conserved region, promoter flanking region), tissue-specific annotations from the Roadmap Epigenomics ProgramADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/s41588-018-0081-4","ISSN":"1546-1718","PMID":"29632380","abstract":"We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.","author":[{"dropping-particle":"","family":"Finucane","given":"Hilary K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reshef","given":"Yakir A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anttila","given":"Verneri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Slowikowski","given":"Kamil","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gusev","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Byrnes","given":"Andrea","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gazal","given":"Steven","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loh","given":"Po-Ru","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lareau","given":"Caleb","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shoresh","given":"Noam","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Genovese","given":"Giulio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saunders","given":"Arpiar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Macosko","given":"Evan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pollack","given":"Samuela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brainstorm Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perry","given":"John R B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buenrostro","given":"Jason D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bernstein","given":"Bradley E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raychaudhuri","given":"Soumya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McCarroll","given":"Steven","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neale","given":"Benjamin M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alkes L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2018","4"]]},"page":"621-629","title":"Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.","type":"article-journal","volume":"50"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁸","plainTextFormattedCitation":"18","previouslyFormattedCitation":"¹⁸"},"properties":{"noteIndex":0},"schema":""}18 and GenoSkylineADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pgen.1006933","ISSN":"1553-7404","PMID":"28742084","abstract":"Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer's disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson's disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at .","author":[{"dropping-particle":"","family":"Lu","given":"Qiongshi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Powles","given":"Ryan L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abdallah","given":"Sarah","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ou","given":"Derek","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Qian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Yiming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Yisi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Boyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mukherjee","given":"Shubhabrata","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crane","given":"Paul K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Hongyu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS genetics","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2017","7"]]},"page":"e1006933","title":"Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.","type":"article-journal","volume":"13"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁹","plainTextFormattedCitation":"19","previouslyFormattedCitation":"¹⁹"},"properties":{"noteIndex":0},"schema":""}19. We also used SNPseaADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btu326","ISSN":"1367-4811","PMID":"24813542","abstract":"UNLABELLED We created a fast, robust and general C+ + implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. AVAILABILITY AND IMPLEMENTATION . SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.","author":[{"dropping-particle":"","family":"Slowikowski","given":"Kamil","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Xinli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raychaudhuri","given":"Soumya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics (Oxford, England)","id":"ITEM-1","issue":"17","issued":{"date-parts":[["2014","9","1"]]},"page":"2496-7","title":"SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci.","type":"article-journal","volume":"30"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁰","plainTextFormattedCitation":"20","previouslyFormattedCitation":"²⁰"},"properties":{"noteIndex":0},"schema":""}20 to estimate the enrichment of specific cell types from genome-wide significant associations using gene expression data. For SNPsea, we performed the standard analyses, and added lung single-cell RNA-seq data from 540 cells extracted from individuals with and without idiopathic pulmonary fibrosisADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1172/jci.insight.90558","ISSN":"2379-3708","PMID":"27942595","abstract":"Idiopathic pulmonary fibrosis (IPF) is a lethal interstitial lung disease characterized by airway remodeling, inflammation, alveolar destruction, and fibrosis. We utilized single-cell RNA sequencing (scRNA-seq) to identify epithelial cell types and associated biological processes involved in the pathogenesis of IPF. Transcriptomic analysis of normal human lung epithelial cells defined gene expression patterns associated with highly differentiated alveolar type 2 (AT2) cells, indicated by enrichment of RNAs critical for surfactant homeostasis. In contrast, scRNA-seq of IPF cells identified 3 distinct subsets of epithelial cell types with characteristics of conducting airway basal and goblet cells and an additional atypical transitional cell that contributes to pathological processes in IPF. Individual IPF cells frequently coexpressed alveolar type 1 (AT1), AT2, and conducting airway selective markers, demonstrating \"indeterminate\" states of differentiation not seen in normal lung development. Pathway analysis predicted aberrant activation of canonical signaling via TGF-β, HIPPO/YAP, P53, WNT, and AKT/PI3K. Immunofluorescence confocal microscopy identified the disruption of alveolar structure and loss of the normal proximal-peripheral differentiation of pulmonary epithelial cells. scRNA-seq analyses identified loss of normal epithelial cell identities and unique contributions of epithelial cells to the pathogenesis of IPF. The present study provides a rich data source to further explore lung health and disease.","author":[{"dropping-particle":"","family":"Xu","given":"Yan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mizuno","given":"Takako","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sridharan","given":"Anusha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Du","given":"Yina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guo","given":"Minzhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tang","given":"Jie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wikenheiser-Brokamp","given":"Kathryn A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perl","given":"Anne-Karina T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Funari","given":"Vincent A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gokey","given":"Jason J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stripp","given":"Barry R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Whitsett","given":"Jeffrey A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"JCI insight","id":"ITEM-1","issue":"20","issued":{"date-parts":[["2016","12","8"]]},"page":"e90558","title":"Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis.","type":"article-journal","volume":"1"},"uris":[""]}],"mendeley":{"formattedCitation":"²¹","plainTextFormattedCitation":"21","previouslyFormattedCitation":"²¹"},"properties":{"noteIndex":0},"schema":""}21 using the processed and normalized dataset available through Gene Expression Omnibus (GSE86618). We tested for enrichment of genes specifically expressed in the four cell types individually among genome-wide significant loci. We also appled three additional single-cell RNA-seq datasets to the SNPsea results, including inflammatory cell types from individuals with lung cancer and normal controls (both in blood and lung)ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.cell.2017.04.014","ISSN":"1097-4172","PMID":"28475900","abstract":"To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor-infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single-cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. VIDEO ABSTRACT.","author":[{"dropping-particle":"","family":"Lavin","given":"Yonit","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kobayashi","given":"Soma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leader","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Amir","given":"El-Ad David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elefant","given":"Naama","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bigenwald","given":"Camille","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Remark","given":"Romain","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sweeney","given":"Robert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Becker","given":"Christian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levine","given":"Jacob H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meinhof","given":"Klaus","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chow","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kim-Shulze","given":"Seunghee","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wolf","given":"Andrea","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Medaglia","given":"Chiara","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Hanjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rytlewski","given":"Julie A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Emerson","given":"Ryan O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Solovyov","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Greenbaum","given":"Benjamin D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sanders","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vignali","given":"Marissa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beasley","given":"Mary Beth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Flores","given":"Raja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gnjatic","given":"Sacha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pe'er","given":"Dana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rahman","given":"Adeeb","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Amit","given":"Ido","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Merad","given":"Miriam","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2017","5","4"]]},"page":"750-765.e17","title":"Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses.","type":"article-journal","volume":"169"},"uris":[""]}],"mendeley":{"formattedCitation":"²²","plainTextFormattedCitation":"22","previouslyFormattedCitation":"²²"},"properties":{"noteIndex":0},"schema":""}22, and murine lungs at E16.5 from LungMAPADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1004575","ISSN":"1553-7358","PMID":"26600239","abstract":"A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, .","author":[{"dropping-particle":"","family":"Guo","given":"Minzhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Hui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Potter","given":"S Steven","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Whitsett","given":"Jeffrey A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Yan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS computational biology","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2015","11"]]},"page":"e1004575","title":"SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.","type":"article-journal","volume":"11"},"uris":[""]}],"mendeley":{"formattedCitation":"²³","plainTextFormattedCitation":"23","previouslyFormattedCitation":"²³"},"properties":{"noteIndex":0},"schema":""}23, using the org.Mm.eg.db packageADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Carlson","given":"Marc","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"note":"R package version 3.6.0","title":"org.Mm.eg.db: Genome wide annotation for Mouse","type":"article"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁴","plainTextFormattedCitation":"24","previouslyFormattedCitation":"²⁴"},"properties":{"noteIndex":0},"schema":""}24 to map locations of orthologous genes.Fine-mapping of independent association signals at genome-wide significant lociWe used Bayesian fine-mapping at each locus to identify the credible set, the set of variants with a 99% probability of containing a causal variant. Briefly, for each genome-wide significant loci we calculated approximate Bayes factorsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1086/519024","ISSN":"0002-9297","PMID":"17668372","abstract":"In light of the vast amounts of genomic data that are now being generated, we propose a new measure, the Bayesian false-discovery probability (BFDP), for assessing the noteworthiness of an observed association. BFDP shares the ease of calculation of the recently proposed false-positive report probability (FPRP) but uses more information, has a noteworthy threshold defined naturally in terms of the costs of false discovery and nondiscovery, and has a sound methodological foundation. In addition, in a multiple-testing situation, it is straightforward to estimate the expected numbers of false discoveries and false nondiscoveries. We provide an in-depth discussion of FPRP, including a comparison with the q value, and examine the empirical behavior of these measures, along with BFDP, via simulation. Finally, we use BFDP to assess the association between 131 single-nucleotide polymorphisms and lung cancer in a case-control study.","author":[{"dropping-particle":"","family":"Wakefield","given":"Jon","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of human genetics","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2007","8"]]},"page":"208-27","title":"A Bayesian measure of the probability of false discovery in genetic epidemiology studies.","type":"article-journal","volume":"81"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁵","plainTextFormattedCitation":"25","previouslyFormattedCitation":"²⁵"},"properties":{"noteIndex":0},"schema":""}25 of association. We then selected variants in each locus, so that their cumulative posterior probability is equal or greater than 0.99. At loci with multiple independent associations, we used statistics from approximate conditional analysis with GCTA software on each index variant adjusting for other independent variants in the loci. Otherwise, we used unconditioned statistics from our meta-analysis.To characterize variant effects in credible sets, we annotated variants in each credible set using Ensembl Variant Effect Predictor through biomartADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nprot.2009.97","ISSN":"1750-2799","PMID":"19617889","abstract":"Genomic experiments produce multiple views of biological systems, among them are DNA sequence and copy number variation, and mRNA and protein abundance. Understanding these systems needs integrated bioinformatic analysis. Public databases such as Ensembl provide relationships and mappings between the relevant sets of probe and target molecules. However, the relationships can be biologically complex and the content of the databases is dynamic. We demonstrate how to use the computational environment R to integrate and jointly analyze experimental datasets, employing BioMart web services to provide the molecule mappings. We also discuss typical problems that are encountered in making gene-to-transcript-to-protein mappings. The approach provides a flexible, programmable and reproducible basis for state-of-the-art bioinformatic data integration.","author":[{"dropping-particle":"","family":"Durinck","given":"Steffen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Spellman","given":"Paul T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Birney","given":"Ewan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huber","given":"Wolfgang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature protocols","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2009"]]},"page":"1184-91","title":"Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt.","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁶","plainTextFormattedCitation":"26","previouslyFormattedCitation":"²⁶"},"properties":{"noteIndex":0},"schema":""}26. We defined deleterious variants as those which resulted in nonsynonymous, stop, or splice variants (terms: transcript_ablation, splice_acceptor_variant, splice_donor_variant, stop_gained, frameshift_variant, stop_lost, start_lost, transcript_amplification, inframe_insertion, inframe_deletion, missense_variant, and protein_altering_variant).Identification of target genesWe used several computational approaches with corresponding available datasets to identify target genes in genome-wide significant loci. We used two methods that utilized gene expression data: 1) S-PrediXcan and 2) DEPICT. We used S-PrediXcanADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association test methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restricts our ability to detect associations. Here we propose an efficient statistical method that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes: MulTiXcan. MulTiXcan integrates evidence across multiple panels while taking into account their correlation. We apply our method to a broad set of complex traits available from the UK Biobank and show that we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed an extension to work on summary statistics: S-MulTiXcan, which we show yields highly concordant results with the individual level version. Results from our analysis as well as software and necessary resources to apply our method are publicly available.","author":[{"dropping-particle":"","family":"Barbeira","given":"Alvaro N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pividori","given":"Milton D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zheng","given":"Jiamao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wheeler","given":"Heather E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nicolae","given":"Dan L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Im","given":"Hae Kyung","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"bioRxiv","id":"ITEM-1","issued":{"date-parts":[["2018","1","1"]]},"title":"Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁷","plainTextFormattedCitation":"27","previouslyFormattedCitation":"²⁷"},"properties":{"noteIndex":0},"schema":""}27 to identify genes which for which genetically regulated expression associated with COPD. We used, as an eQTL and gene expression reference database, data from Lung-eQTL consortiumADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pgen.1003029","ISSN":"1553-7404","PMID":"23209423","abstract":"Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55 × 10(-151)). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.","author":[{"dropping-particle":"","family":"Hao","given":"Ke","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bossé","given":"Yohan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nickle","given":"David C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paré","given":"Peter D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Postma","given":"Dirkje S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laviolette","given":"Michel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sandford","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hackett","given":"Tillie L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Daley","given":"Denise","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hogg","given":"James C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elliott","given":"W Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Couture","given":"Christian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lamontagne","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brandsma","given":"Corry-Anke","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berge","given":"Maarten","non-dropping-particle":"van den","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koppelman","given":"Gerard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reicin","given":"Alise S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nicholson","given":"Donald W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Malkov","given":"Vladislav","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Derry","given":"Jonathan M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Suver","given":"Christine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tsou","given":"Jeffrey A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kulkarni","given":"Amit","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Chunsheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vessey","given":"Rupert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Opiteck","given":"Greg J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Curtis","given":"Sean P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timens","given":"Wim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sin","given":"Don D","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS genetics","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2012"]]},"page":"e1003029","title":"Lung eQTLs to help reveal the molecular underpinnings of asthma.","type":"article-journal","volume":"8"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1093/hmg/ddy091","ISSN":"1460-2083","PMID":"29547942","abstract":"Causal genes of chronic obstructive pulmonary disease (COPD) remain elusive. The current study aims at integrating genome-wide association studies (GWAS) and lung expression quantitative trait loci (eQTL) data to map COPD candidate causal genes and gain biological insights into the recently discovered COPD susceptibility loci. Two complementary genomic datasets on COPD were studied. First, the lung eQTL dataset which included whole-genome gene expression and genotyping data from 1038 individuals. Second, the largest COPD GWAS to date from the International COPD Genetics Consortium (ICGC) with 13 710 cases and 38 062 controls. Methods that integrated GWAS with eQTL signals including transcriptome-wide association study (TWAS), colocalization and Mendelian randomization-based (SMR) approaches were used to map causality genes, i.e. genes with the strongest evidence of being the functional effector at specific loci. These methods were applied at the genome-wide level and at COPD risk loci derived from the GWAS literature. Replication was performed using lung data from GTEx. We collated 129 non-overlapping risk loci for COPD from the GWAS literature. At the genome-wide scale, 12 new COPD candidate genes/loci were revealed and six replicated in GTEx including CAMK2A, DMPK, MYO15A, TNFRSF10A, BTN3A2 and TRBV30. In addition, we mapped candidate causal genes for 60 out of the 129 GWAS-nominated loci and 23 of them were replicated in GTEx. Mapping candidate causal genes in lung tissue represents an important contribution to the genetics of COPD, enriches our biological interpretation of GWAS findings, and brings us closer to clinical translation of genetic associations.","author":[{"dropping-particle":"","family":"Lamontagne","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bérubé","given":"Jean-Christophe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Obeidat","given":"Ma'en","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hobbs","given":"Brian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sakornsakolpat","given":"Phuwanat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jong","given":"Kim","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boezen","given":"H Marike","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"International COPD Genetics Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nickle","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hao","given":"Ke","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timens","given":"Wim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berge","given":"Maarten","non-dropping-particle":"van den","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Joubert","given":"Philippe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laviolette","given":"Michel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sin","given":"Don D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paré","given":"Peter D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bossé","given":"Yohan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Human molecular genetics","id":"ITEM-2","issue":"10","issued":{"date-parts":[["2018","5","15"]]},"page":"1819-1829","title":"Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations.","type":"article-journal","volume":"27"},"uris":[""]}],"mendeley":{"formattedCitation":"^28,29","plainTextFormattedCitation":"28,29","previouslyFormattedCitation":"^28,29"},"properties":{"noteIndex":0},"schema":""}28,29 (1,111 lung tissue samples). DEPICT (Data-driven Expression Prioritized Integration for Complex Traits) uses co-regulation of gene expression and gene-sets to prioritize target genes at associated lociADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ncomms6890","ISSN":"2041-1723","PMID":"25597830","abstract":"The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.","author":[{"dropping-particle":"","family":"Pers","given":"Tune H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karjalainen","given":"Juha M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chan","given":"Yingleong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Westra","given":"Harm-Jan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wood","given":"Andrew R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Jian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lui","given":"Julian C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vedantam","given":"Sailaja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gustafsson","given":"Stefan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Esko","given":"Tonu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frayling","given":"Tim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Speliotes","given":"Elizabeth K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Genetic Investigation of ANthropometric Traits (GIANT) Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boehnke","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raychaudhuri","given":"Soumya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fehrmann","given":"Rudolf S N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hirschhorn","given":"Joel N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Franke","given":"Lude","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature communications","id":"ITEM-1","issued":{"date-parts":[["2015","1","19"]]},"page":"5890","title":"Biological interpretation of genome-wide association studies using predicted gene functions.","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁰","plainTextFormattedCitation":"30","previouslyFormattedCitation":"³⁰"},"properties":{"noteIndex":0},"schema":""}30.We also used additional information on gene regulation, including epigenetic data: 4) regulatory fine mapping, 5) mQTL, and 6) chromosome conformation capture. We used regulatory fine mapping (regfmADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.ajhg.2017.06.001","ISSN":"1537-6605","PMID":"28686857","abstract":"Genome-wide association studies in autoimmune and inflammatory diseases (AID) have uncovered hundreds of loci mediating risk. These associations are preferentially located in non-coding DNA regions and in particular in tissue-specific DNase I hypersensitivity sites (DHSs). While these analyses clearly demonstrate the overall enrichment of disease risk alleles on gene regulatory regions, they are not designed to identify individual regulatory regions mediating risk or the genes under their control, and thus uncover the specific molecular events driving disease risk. To do so we have departed from standard practice by identifying regulatory regions which replicate across samples and connect them to the genes they control through robust re-analysis of public data. We find significant evidence of regulatory potential in 78/301 (26%) risk loci across nine autoimmune and inflammatory diseases, and we find that individual genes are targeted by these effects in 53/78 (68%) of these. Thus, we are able to generate testable mechanistic hypotheses of the molecular changes that drive disease risk.","author":[{"dropping-particle":"","family":"Shooshtari","given":"Parisa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Hailiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cotsapas","given":"Chris","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of human genetics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2017","7","6"]]},"page":"75-86","title":"Integrative Genetic and Epigenetic Analysis Uncovers Regulatory Mechanisms of Autoimmune Disease.","type":"article-journal","volume":"101"},"uris":[""]}],"mendeley":{"formattedCitation":"³¹","plainTextFormattedCitation":"31","previouslyFormattedCitation":"³¹"},"properties":{"noteIndex":0},"schema":""}31) to map open chromatin regions based on DNAse with gene expression of different cell types in the Roadmap Epigenomic Project, and implicate potentially target genes of disease-associated variants. We also searched for overlapping methylation quantitative trait loci (mQTL) data from lung tissueADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1164/rccm.201707-1434OC","ISSN":"1535-4970","PMID":"29313708","abstract":"RATIONALE As the third leading cause of death in the United States, the impact of chronic obstructive pulmonary disease (COPD) makes identification of its molecular mechanisms of great importance. Genome-wide association studies have identified multiple genomic regions associated with COPD. However, genetic variation only explains a small fraction of the susceptibility to COPD and sub-genome-wide significant loci may play a role in pathogenesis. OBJECTIVES Regulatory annotation with epigenetic evidence may give priority for further investigation, particularly for GWAS associations in non-coding regions. We performed integrative genomics analyses using DNA methylation profiling and genome-wide SNP genotyping from lung tissue samples from 90 subjects with COPD and 36 controls. METHODS We performed methylation QTL (mQTL) analyses, testing for SNPs associated with percent DNA methylation and assessed the colocalization of these results with previous COPD GWAS findings using Bayesian methods in the R package coloc to highlight potential regulatory features of the loci. MAIN RESULTS We identified 942,068 unique SNPs and 33,996 unique CpG sites among the significant (5% FDR) cis-mQTL results. The genome-wide significant and sub-threshold (p<10-4) GWAS SNPs were enriched in the significant mQTL SNPs (hypergeometric test p < 0.00001). We observed enrichment for sites located in CpG shores and shelves, but not CpG islands. Using Bayesian colocalization, we identified loci in regions near KCNK3, EEFSEC, PIK3CD, DCDC2C, TCERG1L, FRMD4B and IL27. CONCLUSIONS Colocalization of methylation QTL and GWAS loci provides regulatory characterization of significant and sub-threshold GWAS findings, supporting a role for genetic control of methylation in COPD pathogenesis.","author":[{"dropping-particle":"","family":"Morrow","given":"Jarrett D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Glass","given":"Kimberly","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hersh","given":"Craig P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pinto-Plata","given":"Victor","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Celli","given":"Bartolome","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marchetti","given":"Nathaniel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Criner","given":"Gerard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bueno","given":"Raphael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Washko","given":"George","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Choi","given":"Augustine M K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Quackenbush","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"Edwin K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"DeMeo","given":"Dawn L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of respiratory and critical care medicine","id":"ITEM-1","issued":{"date-parts":[["2018","1","9"]]},"title":"Human Lung DNA Methylation Quantitative Trait Loci Colocalize with COPD Genome-wide Association Loci.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"³²","plainTextFormattedCitation":"32","previouslyFormattedCitation":"³²"},"properties":{"noteIndex":0},"schema":""}32. To determine whether these signals co-localized (rather than being related due to linkage disequilibrium), we performed colocalization analysis between our GWAS and mQTL in genome-wide significant loci using eCAVIARADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.ajhg.2016.10.003","ISSN":"1537-6605","PMID":"27866706","abstract":"The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.","author":[{"dropping-particle":"","family":"Hormozdiari","given":"Farhad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bunt","given":"Martijn","non-dropping-particle":"van de","parse-names":false,"suffix":""},{"dropping-particle":"V","family":"Segrè","given":"Ayellet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Xiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Joo","given":"Jong Wha J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bilow","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sul","given":"Jae Hoon","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sankararaman","given":"Sriram","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pasaniuc","given":"Bogdan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eskin","given":"Eleazar","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of human genetics","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2016","12","1"]]},"page":"1245-1260","title":"Colocalization of GWAS and eQTL Signals Detects Target Genes.","type":"article-journal","volume":"99"},"uris":[""]}],"mendeley":{"formattedCitation":"³³","plainTextFormattedCitation":"33","previouslyFormattedCitation":"³³"},"properties":{"noteIndex":0},"schema":""}33 (eQTL and GWAS CAusal Variants Identification in Associated Regions). We also sought information from publicly available chromosome conformation capture dataADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.celrep.2016.10.061","ISSN":"2211-1247","PMID":"27851967","abstract":"The three-dimensional configuration of DNA is integral to all nuclear processes in eukaryotes, yet our knowledge of the chromosome architecture is still limited. Genome-wide chromosome conformation capture studies have uncovered features of chromatin organization in cultured cells, but genome architecture in human tissues has yet to be explored. Here, we report the most comprehensive survey to date of chromatin organization in human tissues. Through integrative analysis of chromatin contact maps in 21 primary human tissues and cell types, we find topologically associating domains highly conserved in different tissues. We also discover genomic regions that exhibit unusually high levels of local chromatin interactions. These frequently interacting regions (FIREs) are enriched for super-enhancers and are near tissue-specifically expressed genes. They display strong tissue-specificity in local chromatin interactions. Additionally, FIRE formation is partially dependent on CTCF and the Cohesin complex. We further show that FIREs can help annotate the function of non-coding sequence variants.","author":[{"dropping-particle":"","family":"Schmitt","given":"Anthony D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Ming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jung","given":"Inkyung","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Zheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qiu","given":"Yunjiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tan","given":"Catherine L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Yun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Shin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Yiing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barr","given":"Cathy L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ren","given":"Bing","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell reports","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2016"]]},"page":"2042-2059","title":"A Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human Genome.","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁴","plainTextFormattedCitation":"34","previouslyFormattedCitation":"³⁴"},"properties":{"noteIndex":0},"schema":""}34. We obtained statistics of high-confidence contacts in fetal lung fibroblast cell line (IMR90) and human lung tissueADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.celrep.2016.10.061","ISSN":"2211-1247","PMID":"27851967","abstract":"The three-dimensional configuration of DNA is integral to all nuclear processes in eukaryotes, yet our knowledge of the chromosome architecture is still limited. Genome-wide chromosome conformation capture studies have uncovered features of chromatin organization in cultured cells, but genome architecture in human tissues has yet to be explored. Here, we report the most comprehensive survey to date of chromatin organization in human tissues. Through integrative analysis of chromatin contact maps in 21 primary human tissues and cell types, we find topologically associating domains highly conserved in different tissues. We also discover genomic regions that exhibit unusually high levels of local chromatin interactions. These frequently interacting regions (FIREs) are enriched for super-enhancers and are near tissue-specifically expressed genes. They display strong tissue-specificity in local chromatin interactions. Additionally, FIRE formation is partially dependent on CTCF and the Cohesin complex. We further show that FIREs can help annotate the function of non-coding sequence variants.","author":[{"dropping-particle":"","family":"Schmitt","given":"Anthony D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Ming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jung","given":"Inkyung","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Zheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qiu","given":"Yunjiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tan","given":"Catherine L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Yun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Shin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lin","given":"Yiing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barr","given":"Cathy L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ren","given":"Bing","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell reports","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2016"]]},"page":"2042-2059","title":"A Compendium of Chromatin Contact Maps Reveals Spatially Active Regions in the Human Genome.","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁴","plainTextFormattedCitation":"34","previouslyFormattedCitation":"³⁴"},"properties":{"noteIndex":0},"schema":""}34 through HUGInADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btx359","ISSN":"1367-4811","PMID":"28582503","abstract":"Motivation High throughput chromatin conformation capture (3C) technologies, such as Hi-C and ChIA-PET, have the potential to elucidate the functional roles of non-coding variants. However, most of published genome-wide unbiased chromatin organization studies have used cultured cell lines, limiting their generalizability. Results We developed a web browser, HUGIn, to visualize Hi-C data generated from 21 human primary tissues and cell lines. HUGIn enables assessment of chromatin contacts both constitutive across and specific to tissue(s) and/or cell line(s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the understanding of both GWAS and eQTL results and functional genomics data. Availability and implementation HUGIn is available at . Contact yunli@med.unc.edu or hum@. Supplementary information Supplementary data are available at Bioinformatics online.","author":[{"dropping-particle":"","family":"Martin","given":"Joshua S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Zheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reiner","given":"Alex P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mohlke","given":"Karen L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sullivan","given":"Patrick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ren","given":"Bing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Ming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Yun","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics (Oxford, England)","id":"ITEM-1","issue":"23","issued":{"date-parts":[["2017","12","1"]]},"page":"3793-3795","title":"HUGIn: Hi-C Unifying Genomic Interrogator.","type":"article-journal","volume":"33"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁵","plainTextFormattedCitation":"35","previouslyFormattedCitation":"³⁵"},"properties":{"noteIndex":0},"schema":""}35 (Hi-C Unifying Genomic Interrogator). We anchored long range chromatin interactions on top associated variants, and computed statistical significance of contact at each locus. We retained only strongest associations (i.e., smallest P value) for each cell line/primary cell in the analysis. Finally, we searched for signals from non-synonymous variants. We identified coding variants present in the credible set in the GWAS with a high posterior probability. We also searched for rare coding variants, based on exome sequencing results in the COPDGene, Boston Early-Onset COPD, and International COPD Genetics Network studies. Details of the exome sequencing results can be found in [Qiao et al, unpublished manuscript]. In brief, 485 severe COPD cases and 504 smoking resistant controls from the COPDGene study were exome-sequenced and analyzed as case-control data. 1554 subjects ascertained through probands with severe COPD from the Boston Early-Onset COPD study (BEOCOPD) and the ICGN study were exomed and analyzed using the lung function (FEV1) as the outcome. Single-variant analyses were done using Firth and efficient resampling methods (SKAT R packageADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/biostatistics/kxv033","ISSN":"1468-4357","PMID":"26363037","abstract":"For aggregation tests of genes or regions, the set of included variants often have small total minor allele counts (MACs), and this is particularly true when the most deleterious sets of variants are considered. When MAC is low, commonly used asymptotic tests are not well calibrated for binary phenotypes and can have conservative or anti-conservative results and potential power loss. Empirical p-values obtained via resampling methods are computationally costly for highly significant p-values and the results can be conservative due to the discrete nature of resampling tests. Based on the observation that only the individuals containing minor alleles contribute to the score statistics, we develop an efficient resampling method for single and multiple variant score-based tests that can adjust for covariates. Our method can improve computational efficiency >1000-fold over conventional resampling for low MAC variant sets. We ameliorate the conservativeness of results through the use of mid-p-values. Using the estimated minimum achievable p-value for each test, we calibrate QQ plots and provide an effective number of tests. In analysis of a case-control study with deep exome sequence, we demonstrate that our methods are both well calibrated and also reduce computation time significantly compared with resampling methods.","author":[{"dropping-particle":"","family":"Lee","given":"Seunggeun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fuchsberger","given":"Christian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kim","given":"Sehee","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Scott","given":"Laura","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Biostatistics (Oxford, England)","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2016","1"]]},"page":"1-15","title":"An efficient resampling method for calibrating single and gene-based rare variant association analysis in case-control studies.","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁶","plainTextFormattedCitation":"36"},"properties":{"noteIndex":0},"schema":""}36) for the COPDGene data and generalized linear mixed models (GMMAT) for the BEOCOPD-ICGN data. Gene-based analyses were conducted using Burden, SKAT, and SKAT-O tests with asymptotic and efficient resampling methods (SKAT package) combined with Fisher’s method for the COPDGene data, and using SKAT-O tests (MONSTER) for the BEOCOPD-ICGN data. Two variant-filtering criteria were considered: deleterious variants (predicted by FATHMM) with MAF < 0.01, and functional variants (moderate effect predicted by SNPEff) with MAF < 0.05. Gene-based segregation test (GESE) was also applied to the ultra-rare (MAF < 0.1%) and loss-of-function variants in the BEOCOPD-ICGN data on the severe COPD affection status.For each dataset described above, we used Bonferroni-corrected P values, or a fixed posterior probability threshold to determine target genes at each locus. To correct for possible number of genes in each locus, we obtained a list of protein-coding genes within 1 Mb from a top associated variant from biomartADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nprot.2009.97","ISSN":"1750-2799","PMID":"19617889","abstract":"Genomic experiments produce multiple views of biological systems, among them are DNA sequence and copy number variation, and mRNA and protein abundance. Understanding these systems needs integrated bioinformatic analysis. Public databases such as Ensembl provide relationships and mappings between the relevant sets of probe and target molecules. However, the relationships can be biologically complex and the content of the databases is dynamic. We demonstrate how to use the computational environment R to integrate and jointly analyze experimental datasets, employing BioMart web services to provide the molecule mappings. We also discuss typical problems that are encountered in making gene-to-transcript-to-protein mappings. The approach provides a flexible, programmable and reproducible basis for state-of-the-art bioinformatic data integration.","author":[{"dropping-particle":"","family":"Durinck","given":"Steffen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Spellman","given":"Paul T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Birney","given":"Ewan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huber","given":"Wolfgang","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature protocols","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2009"]]},"page":"1184-91","title":"Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt.","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁶","plainTextFormattedCitation":"26","previouslyFormattedCitation":"²⁶"},"properties":{"noteIndex":0},"schema":""}26. For each locus, we used a Bonferroni-corrected P value less than 0.05 (i.e., 0.05 divided by number of genes at that locus) to determine significance for 4data types: gene expression data, chromatin conformation capture data, co-regulation of gene expression, and exome sequencing results. For two remaining datasets, we used a fixed posterior probability threshold of 0.1 for regfm and eCAVIAR. We considered genes passing either of these criteria as target genes. Identification of pathwaysTo identify enriched pathways in COPD-associated loci, we performed gene-set enrichment analysis using DEPICTADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ncomms6890","ISSN":"2041-1723","PMID":"25597830","abstract":"The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.","author":[{"dropping-particle":"","family":"Pers","given":"Tune H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karjalainen","given":"Juha M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chan","given":"Yingleong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Westra","given":"Harm-Jan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wood","given":"Andrew R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Jian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lui","given":"Julian C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vedantam","given":"Sailaja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gustafsson","given":"Stefan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Esko","given":"Tonu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frayling","given":"Tim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Speliotes","given":"Elizabeth K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Genetic Investigation of ANthropometric Traits (GIANT) Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boehnke","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raychaudhuri","given":"Soumya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fehrmann","given":"Rudolf S N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hirschhorn","given":"Joel N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Franke","given":"Lude","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature communications","id":"ITEM-1","issued":{"date-parts":[["2015","1","19"]]},"page":"5890","title":"Biological interpretation of genome-wide association studies using predicted gene functions.","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁰","plainTextFormattedCitation":"30","previouslyFormattedCitation":"³⁰"},"properties":{"noteIndex":0},"schema":""}30. We determined significant gene sets using P value at false discovery rate (FDR) less than 0.05.Effects on COPD-related and other phenotypesCOPD is a complex and heterogeneous disorder, comprised of different biologic processes and specific phenotypic effects. In addition, many loci discovered by GWAS have pleiotropic effects. To identify these effects, we performed analyses of a) overall genetic correlation with other phenotypes, based on GWAS summary statistics; b) genetic association studies of our genome-wide significant findings using COPD-related phenotypes, including a cluster analysis to identify groups of variants that may be acting via similar mechanisms, c) identification of overlapping genetic loci between related disorders (e.g. asthma and pulmonary fibrosis). We estimated genetic correlation between COPD and 832 diseases/traits in LD HubADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btw613","ISSN":"1367-4811","PMID":"27663502","abstract":"MOTIVATION LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. RESULTS In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. AVAILABILITY AND IMPLEMENTATION The web interface and instructions for using LD Hub are available at CONTACT: jie.zheng@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.","author":[{"dropping-particle":"","family":"Zheng","given":"Jie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Erzurumluoglu","given":"A Mesut","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elsworth","given":"Benjamin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kemp","given":"John P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Howe","given":"Laurence","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haycock","given":"Philip C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hemani","given":"Gibran","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tansey","given":"Katherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laurin","given":"Charles","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pourcain","given":"Beate St","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Warrington","given":"Nicole M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Finucane","given":"Hilary K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alkes L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bulik-Sullivan","given":"Brendan K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anttila","given":"Verneri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paternoster","given":"Lavinia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gaunt","given":"Tom R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Evans","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neale","given":"Benjamin M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics (Oxford, England)","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2017"]]},"page":"272-279","title":"LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.","type":"article-journal","volume":"33"},"uris":[""]}],"mendeley":{"formattedCitation":"³⁷","plainTextFormattedCitation":"37","previouslyFormattedCitation":"³⁶"},"properties":{"noteIndex":0},"schema":""}37. For our additional genetic association studies, we tested for associations between our genome-wide significant loci with 121 features (e.g., lung function, computed tomography-derived metrics, biomarkers, and comorbidities). To characterize effects of genome-wide significant loci to these features, we performed hierarchical clustering based on features with at least one nominal significant association. 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To identify features that independently predict cluster membership, we fitted a logistic regression model via penalized maximum likelihood using the glmnet packageADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"1548-7660","PMID":"20808728","abstract":"We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ?(1) (the lasso), ?(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.","author":[{"dropping-particle":"","family":"Friedman","given":"Jerome","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hastie","given":"Trevor","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tibshirani","given":"Rob","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of statistical software","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2010"]]},"page":"1-22","title":"Regularization Paths for Generalized Linear Models via Coordinate Descent.","type":"article-journal","volume":"33"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁰","plainTextFormattedCitation":"40","previouslyFormattedCitation":"³⁹"},"properties":{"noteIndex":0},"schema":""}40. We determined optimal regularization parameters using 10-fold cross validation. To identify overlapping loci between COPD and related disorders, we used gwas-pwADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3570","ISSN":"1546-1718","PMID":"27182965","abstract":"We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at a false discovery rate of 10%) associated with multiple traits. Several loci are associated with multiple phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of the traits, including risk of schizophrenia (rs13107325: log-transformed odds ratio (log OR) = 0.15, P = 2 × 10(-12)) and Parkinson disease (log OR = -0.15, P = 1.6 × 10(-7)), among others. Second, we used these loci to identify traits that have multiple genetic causes in common. For example, variants associated with increased risk of schizophrenia also tended to be associated with increased risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased body mass index causally increases triglyceride levels.","author":[{"dropping-particle":"","family":"Pickrell","given":"Joseph K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berisa","given":"Tomaz","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Jimmy Z","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ségurel","given":"Laure","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tung","given":"Joyce Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hinds","given":"David A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2016"]]},"page":"709-17","title":"Detection and interpretation of shared genetic influences on 42 human traits.","type":"article-journal","volume":"48"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴¹","plainTextFormattedCitation":"41","previouslyFormattedCitation":"⁴⁰"},"properties":{"noteIndex":0},"schema":""}41 to perform pairwise analysis of GWAS. This method searches for shared genomic segmentsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btv546","ISSN":"1367-4811","PMID":"26395773","abstract":"UNLABELLED We present a method to identify approximately independent blocks of linkage disequilibrium in the human genome. These blocks enable automated analysis of multiple genome-wide association studies. AVAILABILITY AND IMPLEMENTATION code: ; data: . CONTACT tberisa@ SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.","author":[{"dropping-particle":"","family":"Berisa","given":"Tomaz","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pickrell","given":"Joseph K","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics (Oxford, England)","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2016","1","15"]]},"page":"283-5","title":"Approximately independent linkage disequilibrium blocks in human populations.","type":"article-journal","volume":"32"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴²","plainTextFormattedCitation":"42","previouslyFormattedCitation":"⁴¹"},"properties":{"noteIndex":0},"schema":""}42 using adaptive significance threshold, allowing detection of sub genome-wide significant loci. We identified shared segments or variants using posterior probability of colocalizing greater than 0.9ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3570","ISSN":"1546-1718","PMID":"27182965","abstract":"We performed a scan for genetic variants associated with multiple phenotypes by comparing large genome-wide association studies (GWAS) of 42 traits or diseases. We identified 341 loci (at a false discovery rate of 10%) associated with multiple traits. Several loci are associated with multiple phenotypes; for example, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of the traits, including risk of schizophrenia (rs13107325: log-transformed odds ratio (log OR) = 0.15, P = 2 × 10(-12)) and Parkinson disease (log OR = -0.15, P = 1.6 × 10(-7)), among others. Second, we used these loci to identify traits that have multiple genetic causes in common. For example, variants associated with increased risk of schizophrenia also tended to be associated with increased risk of inflammatory bowel disease. Finally, we developed a method to identify pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased body mass index causally increases triglyceride levels.","author":[{"dropping-particle":"","family":"Pickrell","given":"Joseph K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berisa","given":"Tomaz","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Jimmy Z","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ségurel","given":"Laure","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tung","given":"Joyce Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hinds","given":"David A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2016"]]},"page":"709-17","title":"Detection and interpretation of shared genetic influences on 42 human traits.","type":"article-journal","volume":"48"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴¹","plainTextFormattedCitation":"41","previouslyFormattedCitation":"⁴⁰"},"properties":{"noteIndex":0},"schema":""}41. We obtained GWAS summary statistics from two previous studies of pulmonary fibrosisADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/s12863-016-0377-2","ISSN":"1471-2156","PMID":"27266705","abstract":"BACKGROUND Fibrotic idiopathic interstitial pneumonias (fIIP) are a group of fatal lung diseases with largely unknown etiology and without definitive treatment other than lung transplant to prolong life. There is strong evidence for the importance of both rare and common genetic risk alleles in familial and sporadic disease. We have previously used genome-wide single nucleotide polymorphism data to identify 10 risk loci for fIIP. Here we extend that work to imputed genome-wide genotypes and conduct new RNA sequencing studies of lung tissue to identify and characterize new fIIP risk loci. RESULTS We performed genome-wide genotype imputation association analyses in 1616 non-Hispanic white (NHW) cases and 4683 NHW controls followed by validation and replication (878 cases, 2017 controls) genotyping and targeted gene expression in lung tissue. Following meta-analysis of the discovery and replication populations, we identified a novel fIIP locus in the HLA region of chromosome 6 (rs7887 P meta = 3.7 × 10(-09)). Imputation of classic HLA alleles identified two in high linkage disequilibrium that are associated with fIIP (DRB1*15:01 P = 1.3 × 10(-7) and DQB1*06:02 P = 6.1 × 10(-8)). Targeted RNA-sequencing of the HLA locus identified 21 genes differentially expressed between fibrotic and control lung tissue (Q < 0.001), many of which are involved in immune and inflammatory response regulation. In addition, the putative risk alleles, DRB1*15:01 and DQB1*06:02, are associated with expression of the DQB1 gene among fIIP cases (Q < 1 × 10(-16)). 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Our examination of this broad set of human tissues revealed many novel genes and re-identified known ones with patterns of regulation in expected as well as unexpected tissues.","author":[{"dropping-particle":"","family":"Barbeira","given":"Alvaro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dickinson","given":"Scott P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Torres","given":"Jason M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Torstenson","given":"Eric S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zheng","given":"Jiamao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wheeler","given":"Heather E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shah","given":"Kaanan P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Edwards","given":"Todd","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nicolae","given":"Dan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cox","given":"Nancy J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Im","given":"Hae Kyung","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"bioRxiv","id":"ITEM-1","issued":{"date-parts":[["2017"]]},"title":"Integrating tissue specific mechanisms into GWAS summary results","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1093/hmg/ddy091","ISSN":"1460-2083","PMID":"29547942","abstract":"Causal genes of chronic obstructive pulmonary disease (COPD) remain elusive. The current study aims at integrating genome-wide association studies (GWAS) and lung expression quantitative trait loci (eQTL) data to map COPD candidate causal genes and gain biological insights into the recently discovered COPD susceptibility loci. Two complementary genomic datasets on COPD were studied. First, the lung eQTL dataset which included whole-genome gene expression and genotyping data from 1038 individuals. Second, the largest COPD GWAS to date from the International COPD Genetics Consortium (ICGC) with 13 710 cases and 38 062 controls. Methods that integrated GWAS with eQTL signals including transcriptome-wide association study (TWAS), colocalization and Mendelian randomization-based (SMR) approaches were used to map causality genes, i.e. genes with the strongest evidence of being the functional effector at specific loci. These methods were applied at the genome-wide level and at COPD risk loci derived from the GWAS literature. Replication was performed using lung data from GTEx. We collated 129 non-overlapping risk loci for COPD from the GWAS literature. At the genome-wide scale, 12 new COPD candidate genes/loci were revealed and six replicated in GTEx including CAMK2A, DMPK, MYO15A, TNFRSF10A, BTN3A2 and TRBV30. In addition, we mapped candidate causal genes for 60 out of the 129 GWAS-nominated loci and 23 of them were replicated in GTEx. Mapping candidate causal genes in lung tissue represents an important contribution to the genetics of COPD, enriches our biological interpretation of GWAS findings, and brings us closer to clinical translation of genetic associations.","author":[{"dropping-particle":"","family":"Lamontagne","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bérubé","given":"Jean-Christophe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Obeidat","given":"Ma'en","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hobbs","given":"Brian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sakornsakolpat","given":"Phuwanat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jong","given":"Kim","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boezen","given":"H Marike","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"International COPD Genetics Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nickle","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hao","given":"Ke","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Timens","given":"Wim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berge","given":"Maarten","non-dropping-particle":"van den","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Joubert","given":"Philippe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laviolette","given":"Michel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sin","given":"Don D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paré","given":"Peter D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bossé","given":"Yohan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Human molecular genetics","id":"ITEM-2","issue":"10","issued":{"date-parts":[["2018","5","15"]]},"page":"1819-1829","title":"Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations.","type":"article-journal","volume":"27"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1038/nn.4618","ISSN":"1546-1726","PMID":"28805813","abstract":"Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders. We found a number of repositioning candidates, many supported by preclinical or clinical evidence. Repositioning candidates for a number of disorders were also significantly enriched for known psychiatric medications or therapies considered in clinical trials. For example, candidates for schizophrenia were enriched for antipsychotics, while those for bipolar disorder were enriched for both antipsychotics and antidepressants. 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We included drug-gene expression signatures from 2,837 compounds in nine cell lines in the analysisADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.cell.2017.10.049","ISSN":"1097-4172","PMID":"29195078","abstract":"We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. 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Profiles.","type":"article-journal","volume":"171"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁷","plainTextFormattedCitation":"47","previouslyFormattedCitation":"⁴⁶"},"properties":{"noteIndex":0},"schema":""}47, hypothesizing that a drug candidate need to produce an opposing or reverse expression signature induced by the disease.Role of the funding sourceNone of the study sponsors had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.ResultsGenome-wide association study of COPDAmong 502,682 participants in UK Biobank, we included 200,794 unrelated individuals of European ancestry passing genetic and phenotypic quality control, with non-missing covariates including cigarette smoke exposure, and identifiable as cases or controls based on spirometry (Supplementary Figure 1 and Supplementary Text). This corresponds to 21,081 cases and 179,713 controls in UK Biobank. In ICGC cohorts, we included 24 studies (14,083 cases and 42,190 controls). Characteristics of these individuals are shown in Supplementary Table 1. We tested associations of moderate to severe COPD and 7,810,595 variants which present in both UK Biobank and ICGC cohorts. We found no evidence of population substructure and cryptic relatedness in either cohorts (linkage disequilibrium score regression (LDSC) intercept 1·027 [s.e. 0·007] and 0·999 [s.e. 0·007] in UK Biobank and ICGC, respectively).In our meta-analysis, we identified 82 loci (defined using 2-Mb windows) at the traditional genome-wide significance value of 5 x 10-8 (Figure 2). Sixteen of 82 loci were previously described as genome-wide significant in COPD, and an additional 31 loci were previously described in association with population-based lung functionADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3787","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","ISSN":"1546-1718","PMID":"28166213","abstract":"Chronic obstructive pulmonary disease (COPD) is characterized by reduced lung function and is the third leading cause of death globally. Through genome-wide association discovery in 48,943 individuals, selected from extremes of the lung function distribution in UK Biobank, and follow-up in 95,375 individuals, we increased the yield of independent signals for lung function from 54 to 97. A genetic risk score was associated with COPD susceptibility (odds ratio per 1 s.d. of the risk score ( approximately 6 alleles) (95% confidence interval) = 1.24 (1.20-1.27), P = 5.05 x 10-49), and we observed a 3.7-fold difference in COPD risk between individuals in the highest and lowest genetic risk score deciles in UK Biobank. The 97 signals show enrichment in genes for development, elastic fibers and epigenetic regulation pathways. 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Marjo-Riitta R; Wielscher, Matthias; James, Alan L; Hui, Jennie; Wareham, Nicholas J; Zhao, Jing Hua; Wilson, James F; Joshi, Peter K; Stubbe, Beate; Rawal, Rajesh; Schulz, Holger; Imboden, Medea; Probst-Hensch, Nicole M; Karrasch, Stefan; Gieger, Christian; Deary, Ian J; Harris, Sarah E; Marten, Jonathan; Rudan, Igor; Enroth, Stefan; Gyllensten, Ulf; Kerr, Shona M; Polasek, Ozren; Kahonen, M; Surakka, Ida; Vitart, Veronique; Hayward, Caroline; Lehtimaki, T; Raitakari, Olli T; Evans, David M; Henderson, A John; Pennell, Craig E; Wang, Carol A; Sly, Peter D; Wan, Emily S; Busch, Robert; Hobbs, Brian D; Litonjua, Augusto A; Sparrow, David W; Gulsvik, Amund; Bakke, Per S; Crapo, James D; Beaty, Terri H; Hansel, Nadia N; Mathias, Rasika A; Ruczinski, Ingo; Barnes, Kathleen C; Bosse, Y; Joubert, Philippe; van den Berge, Maarten; Brandsma, Corry-Anke A; Pare, P D; Sin, Don D; Nickle, David C; Hao, Ke; Gottesman, Omri; Dewey, Frederick E; Bruse, Shannon E; Carey, David J; Kirchner, H Lester; Geisinger-Regeneron Discov, E H R Collaboration; Jonsson, Stefan; Thorleifsson, Gudmar; Jonsdottir, Ingileif; Gislason, Thorarinn; Stefansson, Kari; Schurmann, Claudia; Nadkarni, Girish; Bottinger, Erwin P; Loos, Ruth J F; Walters, Robin G; Chen, Zhengming; Millwood, Iona Y; Vaucher, Julien; Kurmi, Om P; Li, Liming; Hansell, Anna L; Brightling, Chris; Zeggini, Eleftheria; Cho, Michael H; Silverman, Edwin K; Sayers, Ian; Trynka, Gosia; Morris, Andrew P; Strachan, David P; Hall, Ian P; Tobin, Martin D; Understanding Society Scientific Group; Campbell, Archie; Porteous, David J; Jarvelin, Marjo-Riitta R; Wielscher, Matthias; James, Alan L; Hui, Jennie; Wareham, Nicholas J; Zhao, Jing Hua; Wilson, James F; Joshi, Peter K; Stubbe, Beate; Rawal, Rajesh; Schulz, Holger; Imboden, Medea; Probst-Hensch, Nicole M; Karrasch, Stefan; Gieger, Christian; Deary, Ian J; Harris, Sarah E; Marten, Jonathan; Rudan, Igor; Enroth, Stefan; Gyllensten, Ulf; Kerr, Shona M; Polasek, Ozren; K?h?nen, Mika; Surakka, Ida; Vitart, Veronique; Hayward, Caroline; Lehtim?ki, Terho; Raitakari, Olli T; Evans, David M; Henderson, A John; Pennell, Craig E; Wang, Carol A; Sly, Peter D; Wan, Emily S; Busch, Robert; Hobbs, Brian D; Litonjua, Augusto A; Sparrow, David W; Gulsvik, Amund; Bakke, Per S; Crapo, James D; Beaty, Terri H; Hansel, Nadia N; Mathias, Rasika A; Ruczinski, Ingo; Barnes, Kathleen C; Bossé, Yohan; Joubert, Philippe; van den Berge, Maarten; Brandsma, Corry-Anke A; Paré, Peter D; Sin, Don D; Nickle, David C; Hao, Ke; Gottesman, Omri; Dewey, Frederick E; Bruse, Shannon E; Carey, David J; Kirchner, H Lester; Geisinger-Regeneron DiscovEHR Collaboration; Jonsson, Stefan; Thorleifsson, Gudmar; Jonsdottir, Ingileif; Gislason, Thorarinn; Stefansson, Kari; Schurmann, Claudia; Nadkarni, Girish; Bottinger, Erwin P; Loos, Ruth J F; Walters, Robin G; Chen, Zhengming; Millwood, Iona Y; Vaucher, Julien; Kurmi, Om P; Li, Liming; Hansell, Anna L; Brightling, Chris; Zeggini, Eleftheria; Cho, Michael H; Silverman, Edwin K; Sayers, Ian; Trynka, Gosia; Morris, Andrew P; Strachan, David P; Hall, Ian P; Tobin, Martin D)\n\nFrom Duplicate 2 (Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets - Wain, L V; Shrine, N; Artigas, M S; Erzurumluoglu, A M; Noyvert, B; Bossini-Castillo, L; Obeidat, M; Henry, A P; Portelli, M A; Hall, R J; Billington, C K; Rimington, T L; Fenech, A G; John, C; Blake, T; Jackson, V E; Allen, R J; Prins, B P; Understanding Society Scientific, Group; Campbell, A; Porteous, D J; Jarvelin, M R; Wielscher, M; James, A L; Hui, J; Wareham, N J; Zhao, J H; Wilson, J F; Joshi, P K; Stubbe, B; Rawal, R; Schulz, H; Imboden, M; Probst-Hensch, N M; Karrasch, S; Gieger, C; Deary, I J; Harris, S E; Marten, J; Rudan, I; Enroth, S; Gyllensten, U; Kerr, S M; Polasek, O; Kahonen, M; Surakka, I; Vitart, V; Hayward, C; Lehtimaki, T; Raitakari, O T; Evans, D M; Henderson, A J; Pennell, C E; Wang, C A; Sly, P D; Wan, E S; Busch, R; Hobbs, B D; Litonjua, A A; Sparrow, D W; Gulsvik, A; Bakke, P S; Crapo, J D; Beaty, T H; Hansel, N N; Mathias, R A; Ruczinski, I; Barnes, K C; Bosse, Y; Joubert, P; van den Berge, M; Brandsma, C A; Pare, P D; Sin, D D; Nickle, D C; Hao, K; Gottesman, O; Dewey, F E; Bruse, S E; Carey, D J; Kirchner, H L; Geisinger-Regeneron Discov, E H R Collaboration; Jonsson, S; Thorleifsson, G; Jonsdottir, I; Gislason, T; Stefansson, K; Schurmann, C; Nadkarni, G; Bottinger, E P; Loos, R J; Walters, R G; Chen, Z; Millwood, I Y; Vaucher, J; Kurmi, O P; Li, L; Hansell, A L; Brightling, C; Zeggini, E; Cho, M H; Silverman, E K; Sayers, I; Trynka, G; Morris, A P; Strachan, D P; Hall, I P; Tobin, M D)\n\nWain, Louise V\nShrine, Nick\nArtigas, Maria Soler\nErzurumluoglu, A Mesut\nNoyvert, Boris\nBossini-Castillo, Lara\nObeidat, Ma'en\nHenry, Amanda P\nPortelli, Michael A\nHall, Robert J\nBillington, Charlotte K\nRimington, Tracy L\nFenech, Anthony G\nJohn, Catherine\nBlake, Tineka\nJackson, Victoria E\nAllen, Richard J\nPrins, Bram P\nCampbell, Archie\nPorteous, David J\nJarvelin, Marjo-Riitta\nWielscher, Matthias\nJames, Alan L\nHui, Jennie\nWareham, Nicholas J\nZhao, Jing Hua\nWilson, James F\nJoshi, Peter K\nStubbe, Beate\nRawal, Rajesh\nSchulz, Holger\nImboden, Medea\nProbst-Hensch, Nicole M\nKarrasch, Stefan\nGieger, Christian\nDeary, Ian J\nHarris, Sarah E\nMarten, Jonathan\nRudan, Igor\nEnroth, Stefan\nGyllensten, Ulf\nKerr, Shona M\nPolasek, Ozren\nKahonen, Mika\nSurakka, Ida\nVitart, Veronique\nHayward, Caroline\nLehtimaki, Terho\nRaitakari, Olli T\nEvans, David M\nHenderson, A John\nPennell, Craig E\nWang, Carol A\nSly, Peter D\nWan, Emily S\nBusch, Robert\nHobbs, Brian D\nLitonjua, Augusto A\nSparrow, David W\nGulsvik, Amund\nBakke, Per S\nCrapo, James D\nBeaty, Terri H\nHansel, Nadia N\nMathias, Rasika A\nRuczinski, Ingo\nBarnes, Kathleen C\nBosse, Yohan\nJoubert, Philippe\nvan den Berge, Maarten\nBrandsma, Corry-Anke\nPare, Peter D\nSin, Don D\nNickle, David C\nHao, Ke\nGottesman, Omri\nDewey, Frederick E\nBruse, Shannon E\nCarey, David J\nKirchner, H Lester\nJonsson, Stefan\nThorleifsson, Gudmar\nJonsdottir, Ingileif\nGislason, Thorarinn\nStefansson, Kari\nSchurmann, Claudia\nNadkarni, Girish\nBottinger, Erwin P\nLoos, Ruth J F\nWalters, Robin G\nChen, Zhengming\nMillwood, Iona Y\nVaucher, Julien\nKurmi, Om P\nLi, Liming\nHansell, Anna L\nBrightling, Chris\nZeggini, Eleftheria\nCho, Michael H\nSilverman, Edwin K\nSayers, Ian\nTrynka, Gosia\nMorris, Andrew P\nStrachan, David P\nHall, Ian P\nTobin, Martin D\neng\nMR/K026992/1/Medical Research Council/United Kingdom\nMC_UU_12013/4/Medical Research Council/United Kingdom\nCZD/16/6/4/Chief Scientist Office/United Kingdom\nMC_PC_U127561128/Medical Research Council/United Kingdom\nG0902313/Medical Research Council/United Kingdom\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):416-425. doi: 10.1038/ng.3787. Epub 2017 Feb 6.","page":"416-425","title":"Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets","type":"article-journal","volume":"49"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴⁸","plainTextFormattedCitation":"48","previouslyFormattedCitation":"⁴⁷"},"properties":{"noteIndex":0},"schema":""}48 (Supplementary Table 2). This left 35 loci that were novel for COPD and lung function (Table 1). We then sought to replicate these novel loci in independent cohorts. Given the strong genetic correlation between population-based lung function and COPD, we performed a lookup of these loci for FEV1 or FEV1/FVC in SpiroMeta. We identified 13 loci - C1orf87, DENND2D, DDX1, SLMAP, BTC, FGF18, CITED2, ITGB8, OBFC1, ARNTL, SERP2, DTWD1, and ADAMTSL3 (one-sided P < 0.05/37; Table 1). Although not meeting the strict Bonferroni threshold, additional 14 novel loci were nominally significant in SpiroMeta (consistent direction of effect and one-sided P < 0·05) including ASAP2, EML4, VGLL4, ADCY5, HSPA4, CCDC69, RREB1, ID4, IER3, RFX6, MFHAS1, COL15A1, TEPP, and THRA (Table 1).In our overall meta-analysis, all 82 of the genome-wide significant loci showed consistent direction of effect with either FEV1 or FEV1/FVC ratio in SpiroMeta (Supplementary Text). Given the evidence of enrichment for prior signals and overall genome-wide significant results, we performed all subsequent analysis using the overall GWAS, including all 82 loci genome-wide significant in discovery, recognizing that some of our signals could be false positives. Our 82 genome-wide significant variants explain up to 7·0 % of the phenotypic variance in liability scale, using a 10% prevalence of COPD, and acknowledging that these effects may be overestimated. This is a 48% increase in COPD phenotypic variance explained by genetic loci compared to the 4·7% explained by 22 loci reported in a recent GWAS of COPDADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3752","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","ISSN":"1546-1718","PMID":"28166215","abstract":"Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. We performed a genetic association study in 15,256 cases and 47,936 controls, with replication of select top results (P < 5 x 10-6) in 9,498 cases and 9,748 controls. In the combined meta-analysis, we identified 22 loci associated at genome-wide significance, including 13 new associations with COPD. Nine of these 13 loci have been associated with lung function in general population samples, while 4 (EEFSEC, DSP, MTCL1, and SFTPD) are new. We noted two loci shared with pulmonary fibrosis (FAM13A and DSP) but that had opposite risk alleles for COPD. None of our loci overlapped with genome-wide associations for asthma, although one locus has been implicated in joint susceptibility to asthma and obesity. We also identified genetic correlation between COPD and asthma. Our findings highlight new loci associated with COPD, demonstrate the importance of specific loci associated with lung function to COPD, and identify potential regions of genetic overlap between COPD and other respiratory diseases.","author":[{"dropping-particle":"","family":"Hobbs","given":"Brian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jong","given":"Kim","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lamontagne","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bosse","given":"Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shrine","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Artigas","given":"María 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Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Paré, Peter D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; COPDGene Investigators; ECLIPSE Investigators; LifeLines Investigators; SPIROMICS Research Group; International COPD Genetics Network Investigators; UK BiLEVE Investigators; International COPD Genetics Consortium; Bosse, Y; Shrine, Nick; Artigas, María Soler; Wain, Louise V; Hall, Ian P; Jackson, Victoria E; Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Pare, P D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Consortium Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium Network Investigators)\n\nFrom Duplicate 1 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.\n\nFrom Duplicate 2 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.","page":"426-432","title":"Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis","type":"article-journal","volume":"49"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵","plainTextFormattedCitation":"5","previouslyFormattedCitation":"⁵"},"properties":{"noteIndex":0},"schema":""}5. Relationship of genome-wide significant variants to smokingOf the 82 identified loci, three were associated with cigarette smoking in the UK Biobank after Bonferroni correction; the strongest association was for the known 15q25 locus. The next most highly associated locus was IER3 (P = 9·2 x 10-5) at 6p21; however, at this locus, the association with lung function in never-smokers in the UK Biobank was highly significant (P = 6·7 x 10-13). In addition, the SPPL2C (17q21) locus at 17q21 was associated with smoking (P=1·4 x 10-4) and was also significant in the analysis of never-smokers (2·8 x 10-5).Identification of secondary association signalsWe then used approximate conditional and joint analysis to find secondary signals at each of the 82 genome-wide significant loci. We found secondary signals at 50 loci, with a maximum of five secondary signals identified at the RREB1 locus (Supplementary Table 3). Twenty of these signals were genome-wide significant, resulting in a total of 102 independent variants reaching traditional genome-wide significance (< 5 x 10-8) in our study. Tissue and specific cell typesIn determining the tissue in which COPD genetic variants function to increase risk to COPD, lung is the obvious tissue to consider; however, COPD is a systemic diseaseADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1080/15412550801941349","ISSN":"1541-2555","PMID":"18415812","abstract":"Chronic obstructive pulmonary disease (COPD) represents an important and increasing burden throughout the world. Classically, COPD has been considered a respiratory condition only, mainly caused by tobacco smoking. However, COPD has important manifestations beyond the lungs, the so-called systemic effects. These include unintentional weight loss, skeletal muscle dysfunction, an increased risk of cardiovascular disease, osteoporosis, and depression, among others. Low-grade, chronic systemic inflammation is one of the key mechanisms underlying these systemic effects. Because these extra-pulmonary manifestations of COPD are common and/or may have significant implications for the patient wellbeing and prognosis, they warrant systematic screening and appropriate management in order to provide optimal medical care.","author":[{"dropping-particle":"","family":"Agusti","given":"Alvar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Soriano","given":"Joan B","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"COPD","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2008","4"]]},"page":"133-8","title":"COPD as a systemic disease.","type":"article-journal","volume":"5"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1183/09031936.00128008","ISSN":"1399-3003","PMID":"19407051","abstract":"Increasing evidence indicates that chronic obstructive pulmonary disease (COPD) is a complex disease involving more than airflow obstruction. Airflow obstruction has profound effects on cardiac function and gas exchange with systemic consequences. In addition, as COPD results from inflammation and/or alterations in repair mechanisms, the \"spill-over\" of inflammatory mediators into the circulation may result in important systemic manifestations of the disease, such as skeletal muscle wasting and cachexia. Systemic inflammation may also initiate or worsen comorbid diseases, such as ischaemic heart disease, heart failure, osteoporosis, normocytic anaemia, lung cancer, depression and diabetes. Comorbid diseases potentiate the morbidity of COPD, leading to increased hospitalisations, mortality and healthcare costs. Comorbidities complicate the management of COPD and need to be evaluated carefully. Current therapies for comorbid diseases, such as statins and peroxisome proliferator-activated receptor-agonists, may provide unexpected benefits for COPD patients. Treatment of COPD inflammation may concomitantly treat systemic inflammation and associated comorbidities. However, new broad-spectrum anti-inflammatory treatments, such as phosphodiesterase 4 inhibitors, have significant side-effects so it may be necessary to develop inhaled drugs in the future. Another approach is the reversal of corticosteroid resistance, for example with effective antioxidants. More research is needed on COPD comorbidities and their treatment.","author":[{"dropping-particle":"","family":"Barnes","given":"P J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Celli","given":"B R","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The European respiratory journal","id":"ITEM-2","issue":"5","issued":{"date-parts":[["2009","5"]]},"page":"1165-85","title":"Systemic manifestations and comorbidities of COPD.","type":"article-journal","volume":"33"},"uris":[""]}],"mendeley":{"formattedCitation":"^49,50","plainTextFormattedCitation":"49,50","previouslyFormattedCitation":"^48,49"},"properties":{"noteIndex":0},"schema":""}49,50 and the specific cell-types underlying disease pathogenesis are largely unknown. Furthermore, available databases often include specific cell types (e.g. smooth muscle) from non-lung organs (e.g. the gastrointestinal tract). To identify putative causal tissues and cell types, we assessed the enrichment of our genome-wide significant COPD loci in ensemble tissue signaturesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pgen.1006933","ISSN":"1553-7404","PMID":"28742084","abstract":"Continuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer's disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson's disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at .","author":[{"dropping-particle":"","family":"Lu","given":"Qiongshi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Powles","given":"Ryan L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abdallah","given":"Sarah","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ou","given":"Derek","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Qian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Yiming","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Yisi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Wei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Boyang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mukherjee","given":"Shubhabrata","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crane","given":"Paul K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Hongyu","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS genetics","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2017","7"]]},"page":"e1006933","title":"Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.","type":"article-journal","volume":"13"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁹","plainTextFormattedCitation":"19","previouslyFormattedCitation":"¹⁹"},"properties":{"noteIndex":0},"schema":""}19, tissue-specific epigenomic marksADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/s41588-018-0081-4","ISSN":"1546-1718","PMID":"29632380","abstract":"We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.","author":[{"dropping-particle":"","family":"Finucane","given":"Hilary K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Reshef","given":"Yakir A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anttila","given":"Verneri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Slowikowski","given":"Kamil","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gusev","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Byrnes","given":"Andrea","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gazal","given":"Steven","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loh","given":"Po-Ru","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lareau","given":"Caleb","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shoresh","given":"Noam","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Genovese","given":"Giulio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saunders","given":"Arpiar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Macosko","given":"Evan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pollack","given":"Samuela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brainstorm Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perry","given":"John R B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Buenrostro","given":"Jason D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bernstein","given":"Bradley E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raychaudhuri","given":"Soumya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McCarroll","given":"Steven","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neale","given":"Benjamin M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alkes L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2018","4"]]},"page":"621-629","title":"Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types.","type":"article-journal","volume":"50"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁸","plainTextFormattedCitation":"18","previouslyFormattedCitation":"¹⁸"},"properties":{"noteIndex":0},"schema":""}18, and genome-wide gene expression patternsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btu326","ISSN":"1367-4811","PMID":"24813542","abstract":"UNLABELLED We created a fast, robust and general C+ + implementation of a single-nucleotide polymorphism (SNP) set enrichment algorithm to identify cell types, tissues and pathways affected by risk loci. It tests trait-associated genomic loci for enrichment of specificity to conditions (cell types, tissues and pathways). We use a non-parametric statistical approach to compute empirical P-values by comparison with null SNP sets. As a proof of concept, we present novel applications of our method to four sets of genome-wide significant SNPs associated with red blood cell count, multiple sclerosis, celiac disease and HDL cholesterol. AVAILABILITY AND IMPLEMENTATION . SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.","author":[{"dropping-particle":"","family":"Slowikowski","given":"Kamil","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Xinli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raychaudhuri","given":"Soumya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics (Oxford, England)","id":"ITEM-1","issue":"17","issued":{"date-parts":[["2014","9","1"]]},"page":"2496-7","title":"SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci.","type":"article-journal","volume":"30"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁰","plainTextFormattedCitation":"20","previouslyFormattedCitation":"²⁰"},"properties":{"noteIndex":0},"schema":""}20. Lung tissue showed the most significant enrichment (OR 9·25, P=1·36 x 10-9), as previously described, though significant enrichment was also seen in heart (OR 6·85, P=3·83 x 10-8) and the gastrointestinal (GI) tract (OR 5·53, P=6·45 x 10-11). In an analysis of enriched epigenomic marks, the most significant enrichment was in fetal lung and GI smooth muscle DNase (P= 6·75 x 10-8) and H3K4me1 (P= 7·31 x 10-7) (Supplementary Table 4). To further identify lung-specific cell types, we used SNPSea to examine gene expression from single-cell RNA-seq. We found nominally significant enrichment of alveolar type II and basal cells (P=0·016 and P=0·042, respectively) in 82 genome-wide signficiant associations using gene expression data from respiratory cell typesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1172/jci.insight.90558","ISSN":"2379-3708","PMID":"27942595","abstract":"Idiopathic pulmonary fibrosis (IPF) is a lethal interstitial lung disease characterized by airway remodeling, inflammation, alveolar destruction, and fibrosis. We utilized single-cell RNA sequencing (scRNA-seq) to identify epithelial cell types and associated biological processes involved in the pathogenesis of IPF. Transcriptomic analysis of normal human lung epithelial cells defined gene expression patterns associated with highly differentiated alveolar type 2 (AT2) cells, indicated by enrichment of RNAs critical for surfactant homeostasis. In contrast, scRNA-seq of IPF cells identified 3 distinct subsets of epithelial cell types with characteristics of conducting airway basal and goblet cells and an additional atypical transitional cell that contributes to pathological processes in IPF. Individual IPF cells frequently coexpressed alveolar type 1 (AT1), AT2, and conducting airway selective markers, demonstrating \"indeterminate\" states of differentiation not seen in normal lung development. Pathway analysis predicted aberrant activation of canonical signaling via TGF-β, HIPPO/YAP, P53, WNT, and AKT/PI3K. Immunofluorescence confocal microscopy identified the disruption of alveolar structure and loss of the normal proximal-peripheral differentiation of pulmonary epithelial cells. scRNA-seq analyses identified loss of normal epithelial cell identities and unique contributions of epithelial cells to the pathogenesis of IPF. The present study provides a rich data source to further explore lung health and disease.","author":[{"dropping-particle":"","family":"Xu","given":"Yan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mizuno","given":"Takako","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sridharan","given":"Anusha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Du","given":"Yina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guo","given":"Minzhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tang","given":"Jie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wikenheiser-Brokamp","given":"Kathryn A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perl","given":"Anne-Karina T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Funari","given":"Vincent A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gokey","given":"Jason J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stripp","given":"Barry R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Whitsett","given":"Jeffrey A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"JCI insight","id":"ITEM-1","issue":"20","issued":{"date-parts":[["2016","12","8"]]},"page":"e90558","title":"Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis.","type":"article-journal","volume":"1"},"uris":[""]}],"mendeley":{"formattedCitation":"²¹","plainTextFormattedCitation":"21","previouslyFormattedCitation":"²¹"},"properties":{"noteIndex":0},"schema":""}21 (Supplementary Figure 4). We were unable to identify a significant signal for enrichment in humanADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.cell.2017.04.014","ISSN":"1097-4172","PMID":"28475900","abstract":"To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor-infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single-cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. VIDEO ABSTRACT.","author":[{"dropping-particle":"","family":"Lavin","given":"Yonit","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kobayashi","given":"Soma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leader","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Amir","given":"El-Ad David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elefant","given":"Naama","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bigenwald","given":"Camille","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Remark","given":"Romain","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sweeney","given":"Robert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Becker","given":"Christian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levine","given":"Jacob H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meinhof","given":"Klaus","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chow","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kim-Shulze","given":"Seunghee","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wolf","given":"Andrea","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Medaglia","given":"Chiara","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Hanjie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rytlewski","given":"Julie A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Emerson","given":"Ryan O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Solovyov","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Greenbaum","given":"Benjamin D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sanders","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vignali","given":"Marissa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beasley","given":"Mary Beth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Flores","given":"Raja","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gnjatic","given":"Sacha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pe'er","given":"Dana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rahman","given":"Adeeb","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Amit","given":"Ido","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Merad","given":"Miriam","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cell","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2017","5","4"]]},"page":"750-765.e17","title":"Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses.","type":"article-journal","volume":"169"},"uris":[""]}],"mendeley":{"formattedCitation":"²²","plainTextFormattedCitation":"22","previouslyFormattedCitation":"²²"},"properties":{"noteIndex":0},"schema":""}22 and mouse lungADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pcbi.1004575","ISSN":"1553-7358","PMID":"26600239","abstract":"A major challenge in developmental biology is to understand the genetic and cellular processes/programs driving organ formation and differentiation of the diverse cell types that comprise the embryo. While recent studies using single cell transcriptome analysis illustrate the power to measure and understand cellular heterogeneity in complex biological systems, processing large amounts of RNA-seq data from heterogeneous cell populations creates the need for readily accessible tools for the analysis of single-cell RNA-seq (scRNA-seq) profiles. The present study presents a generally applicable analytic pipeline (SINCERA: a computational pipeline for SINgle CEll RNA-seq profiling Analysis) for processing scRNA-seq data from a whole organ or sorted cells. The pipeline supports the analysis for: 1) the distinction and identification of major cell types; 2) the identification of cell type specific gene signatures; and 3) the determination of driving forces of given cell types. We applied this pipeline to the RNA-seq analysis of single cells isolated from embryonic mouse lung at E16.5. Through the pipeline analysis, we distinguished major cell types of fetal mouse lung, including epithelial, endothelial, smooth muscle, pericyte, and fibroblast-like cell types, and identified cell type specific gene signatures, bioprocesses, and key regulators. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, .","author":[{"dropping-particle":"","family":"Guo","given":"Minzhe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wang","given":"Hui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Potter","given":"S Steven","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Whitsett","given":"Jeffrey A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xu","given":"Yan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS computational biology","id":"ITEM-1","issue":"11","issued":{"date-parts":[["2015","11"]]},"page":"e1004575","title":"SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.","type":"article-journal","volume":"11"},"uris":[""]}],"mendeley":{"formattedCitation":"²³","plainTextFormattedCitation":"23","previouslyFormattedCitation":"²³"},"properties":{"noteIndex":0},"schema":""}23 inflammatory cells.Fine-mapping of associated lociTo identify the most likely causal variants, we performed fine mapping using Bayesian credible setsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1086/519024","ISSN":"0002-9297","PMID":"17668372","abstract":"In light of the vast amounts of genomic data that are now being generated, we propose a new measure, the Bayesian false-discovery probability (BFDP), for assessing the noteworthiness of an observed association. BFDP shares the ease of calculation of the recently proposed false-positive report probability (FPRP) but uses more information, has a noteworthy threshold defined naturally in terms of the costs of false discovery and nondiscovery, and has a sound methodological foundation. In addition, in a multiple-testing situation, it is straightforward to estimate the expected numbers of false discoveries and false nondiscoveries. We provide an in-depth discussion of FPRP, including a comparison with the q value, and examine the empirical behavior of these measures, along with BFDP, via simulation. Finally, we use BFDP to assess the association between 131 single-nucleotide polymorphisms and lung cancer in a case-control study.","author":[{"dropping-particle":"","family":"Wakefield","given":"Jon","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of human genetics","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2007","8"]]},"page":"208-27","title":"A Bayesian measure of the probability of false discovery in genetic epidemiology studies.","type":"article-journal","volume":"81"},"uris":[""]}],"mendeley":{"formattedCitation":"²⁵","plainTextFormattedCitation":"25","previouslyFormattedCitation":"²⁵"},"properties":{"noteIndex":0},"schema":""}25. Including 160 potential primary and secondary association signals, and excluding variants in the MHC region, 61 independent signals had a 99% credible set with fewer than 50 variants; 34 signals had credible sets with fewer than 20 variants. Only the association signal at NPNT (4q24) could be fine-mapped to single variant; however, in 17 other loci, a single variant had posterior probability of driving association (PPA) greater than 60% (Supplementary Table 5). Functional annotations of variants with PPA >= 60% are shown in Supplementary Table 5. Most variants overlapped genic enhancers of lung-related cell types (e.g., fetal lung fibroblasts, fetal lung, and adult lung fibroblasts) and were predicted to alter transcription binding motifs. Of 57 credible sets with fewer than 50 variants, nine sets contained at least one deleterious variant. These deleterious variants included 1) missense variants affecting TNS1, RIN3, GPR126, ADAM19, ATP13A2, BTC, and CRLF3; 2) a variant resulting in a premature stop codon affecting ITGB8; and 3) a splice donor variant affecting a lincRNA - AP003059.2.Candidate target genesIn most cases, the closest gene to a lead SNP will not be the gene most likely to be the causal or effector gene of disease-associated variantsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.ajhg.2017.06.005","ISSN":"1537-6605","PMID":"28686856","abstract":"Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.","author":[{"dropping-particle":"","family":"Visscher","given":"Peter M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wray","given":"Naomi R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Qian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sklar","given":"Pamela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"McCarthy","given":"Mark I","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brown","given":"Matthew A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yang","given":"Jian","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of human genetics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2017","7","6"]]},"page":"5-22","title":"10 Years of GWAS Discovery: Biology, Function, and Translation.","type":"article-journal","volume":"101"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1093/hmg/ddr569","ISSN":"1460-2083","PMID":"22140090","abstract":"Multiple intergenic single-nucleotide polymorphisms (SNPs) near hedgehog interacting protein (HHIP) on chromosome 4q31 have been strongly associated with pulmonary function levels and moderate-to-severe chronic obstructive pulmonary disease (COPD). However, whether the effects of variants in this region are related to HHIP or another gene has not been proven. We confirmed genetic association of SNPs in the 4q31 COPD genome-wide association study (GWAS) region in a Polish cohort containing severe COPD cases and healthy smoking controls (P = 0.001 to 0.002). We found that HHIP expression at both mRNA and protein levels is reduced in COPD lung tissues. We identified a genomic region located ～85 kb upstream of HHIP which contains a subset of associated SNPs, interacts with the HHIP promoter through a chromatin loop and functions as an HHIP enhancer. The COPD risk haplotype of two SNPs within this enhancer region (rs6537296A and rs1542725C) was associated with statistically significant reductions in HHIP promoter activity. Moreover, rs1542725 demonstrates differential binding to the transcription factor Sp3; the COPD-associated allele exhibits increased Sp3 binding, which is consistent with Sp3's usual function as a transcriptional repressor. Thus, increased Sp3 binding at a functional SNP within the chromosome 4q31 COPD GWAS locus leads to reduced HHIP expression and increased susceptibility to COPD through distal transcriptional regulation. Together, our findings reveal one mechanism through which SNPs upstream of the HHIP gene modulate the expression of HHIP and functionally implicate reduced HHIP gene expression in the pathogenesis of COPD.","author":[{"dropping-particle":"","family":"Zhou","given":"Xiaobo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Baron","given":"Rebecca M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hardin","given":"Megan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zielinski","given":"Jan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hawrylkiewicz","given":"Iwona","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sliwinski","given":"Pawel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hersh","given":"Craig P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mancini","given":"John D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Ke","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thibault","given":"Derek","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Donahue","given":"Amy L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klanderman","given":"Barbara J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rosner","given":"Bernard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Raby","given":"Benjamin A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Quan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Geldart","given":"Adriana M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Layne","given":"Matthew D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perrella","given":"Mark A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weiss","given":"Scott T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Choi","given":"Augustine M K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"Edwin K","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Human molecular genetics","id":"ITEM-2","issue":"6","issued":{"date-parts":[["2012","3","15"]]},"page":"1325-35","title":"Identification of a chronic obstructive pulmonary disease genetic determinant that regulates HHIP.","type":"article-journal","volume":"21"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1056/NEJMc1513316","ISSN":"1533-4406","PMID":"26760096","author":[{"dropping-particle":"","family":"Claussnitzer","given":"Melina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hui","given":"Chi-Chung","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kellis","given":"Manolis","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The New England journal of medicine","id":"ITEM-3","issue":"2","issued":{"date-parts":[["2016","1","14"]]},"page":"192-3","title":"FTO Obesity Variant and Adipocyte Browning in Humans.","type":"article-journal","volume":"374"},"uris":[""]}],"mendeley":{"formattedCitation":"^51–53","plainTextFormattedCitation":"51–53","previouslyFormattedCitation":"^50–52"},"properties":{"noteIndex":0},"schema":""}51–53. Thus, to identify the potential effector genes underlying these genetic associations, we integrated additional molecular information including gene expression; gene regulation (open chromatin and methylation data), chromatin interaction, co-regulation of gene expression with gene sets and coding variant data.At 82 loci, 481 genes were implicated by either gene expression (Bonferroni corrected at locus level), methylation (CLPP > 0.1), significant contact (most significant gene per cell types with P < 0.05), open chromatin regions (PP > 0.1), co-regulation of gene expression (FDR < 0.05), or deleterious variants detected by exome analysis. Excluding loci in the extended MHC region, the median numbers of potentially implicated genes per locus was four with a maximum of 18 genes (7q22.1). The median distance of implicated genes to top associated variants was 336 Kb, restricting to genes within 1 Mb of top associated variants. Among 82 loci, 59 loci (72%) included the nearest gene. Of 481 genes, 106 were implicated by at least two different sources. We identified 19 genes which were region-wise Bonferroni significant in previously published exome sequencing data. We summarize all genes implicated using these approaches in Supplementary Table 6. Associated pathwaysTo gain further functional insight of associated genetic loci, we performed gene-set enrichment analysis using DEPICT. Among 165 enriched gene sets at FDR < 0.05, 44% of them were related to the developmental process term. Many gene sets were related to lung development including lung alveolus development (nominal P= 0.0003) and lung morphogenesis (nominal P= 0.0005). Interestingly, we also found enrichment of extracellular matrix -related pathways including laminin binding, integrin binding, mesenchyme development, cell-matrix adhesion, and actin filament bundles. Furthermore, there was enrichment of signaling pathways including histone deacetylase binding, Wnt receptor signaling pathway, SMAD binding, the MAPK cascade, and transmembrane receptor protein serine/threonine kinase signaling pathway. Full enrichment analysis results including top genes for each DEPICT gene set are shown in Supplementary Table 7.Genetic correlation of COPD and related traitsTo gain further insight of genetic contribution of COPD and other traits, we estimated genetic correlation among traits using LDSC in LD hubADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bioinformatics/btw613","ISSN":"1367-4811","PMID":"27663502","abstract":"MOTIVATION LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. RESULTS In this manuscript, we describe LD Hub - a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. AVAILABILITY AND IMPLEMENTATION The web interface and instructions for using LD Hub are available at CONTACT: jie.zheng@bristol.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.","author":[{"dropping-particle":"","family":"Zheng","given":"Jie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Erzurumluoglu","given":"A Mesut","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Elsworth","given":"Benjamin L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kemp","given":"John P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Howe","given":"Laurence","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Haycock","given":"Philip C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hemani","given":"Gibran","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tansey","given":"Katherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laurin","given":"Charles","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pourcain","given":"Beate St","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Warrington","given":"Nicole M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Finucane","given":"Hilary K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alkes L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bulik-Sullivan","given":"Brendan K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anttila","given":"Verneri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paternoster","given":"Lavinia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gaunt","given":"Tom R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Evans","given":"David M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neale","given":"Benjamin M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Bioinformatics (Oxford, England)","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2017"]]},"page":"272-279","title":"LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis.","type":"article-journal","volume":"33"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/ng.3406","ISSN":"1546-1718","PMID":"26414676","abstract":"Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.","author":[{"dropping-particle":"","family":"Bulik-Sullivan","given":"Brendan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Finucane","given":"Hilary K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anttila","given":"Verneri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gusev","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Day","given":"Felix R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loh","given":"Po-Ru","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"ReproGen Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Psychiatric Genomics Consortium","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Duncan","given":"Laramie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perry","given":"John R B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Patterson","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Robinson","given":"Elise B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Daly","given":"Mark J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alkes L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Neale","given":"Benjamin M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature genetics","id":"ITEM-2","issue":"11","issued":{"date-parts":[["2015","11"]]},"page":"1236-41","title":"An atlas of genetic correlations across human diseases and traits.","type":"article-journal","volume":"47"},"uris":[""]}],"mendeley":{"formattedCitation":"^37,54","plainTextFormattedCitation":"37,54","previouslyFormattedCitation":"^36,53"},"properties":{"noteIndex":0},"schema":""}37,54. In addition to our previously reported correlations between COPD and lung function (FEV1 and FEV1/FVC), asthma, and height are significant (Supplementary Figure 5 and Supplementary Table 8). We additionally identified significant correlation of COPD and lung cancer (rg=0.184, P=0.003). We also tested genetic correlation between COPD and other comorbidities. We were not able to demonstrate genetic correlation of COPD with bone mineral density, major depressive disorder, coronary artery disease, or type 2 diabetes mellitus (Supplementary Figure 5 and Supplementary Table 8) We also identified nominally significant genetic correlation of COPD with hand grip, angina, gastroesophageal reflux disease, multiple sclerosis, and schizophrenia (Supplementary Figure 5 and Supplementary Table 8).Identification of loci overlapping with asthma and pulmonary fibrosisBased on our previous identification of genetic overlap of COPD with asthma, and COPD with pulmonary fibrosis, we applied a Bayesian method to identify overlap (gwas-pw) of COPD associations from our current GWAS with previous GWASs of asthma and pulmonary fibrosisADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/s41588-017-0014-7","ISSN":"1546-1718","PMID":"29273806","abstract":"We examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 asthma cases, 118,538 controls) of individuals from ethnically diverse populations. We identified five new asthma loci, found two new associations at two known asthma loci, established asthma associations at two loci previously implicated in the comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. 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lung transplant to prolong life. There is strong evidence for the importance of both rare and common genetic risk alleles in familial and sporadic disease. We have previously used genome-wide single nucleotide polymorphism data to identify 10 risk loci for fIIP. Here we extend that work to imputed genome-wide genotypes and conduct new RNA sequencing studies of lung tissue to identify and characterize new fIIP risk loci. RESULTS We performed genome-wide genotype imputation association analyses in 1616 non-Hispanic white (NHW) cases and 4683 NHW controls followed by validation and replication (878 cases, 2017 controls) genotyping and targeted gene expression in lung tissue. Following meta-analysis of the discovery and replication populations, we identified a novel fIIP locus in the HLA region of chromosome 6 (rs7887 P meta = 3.7 × 10(-09)). Imputation of classic HLA alleles identified two in high linkage disequilibrium that are associated with fIIP (DRB1*15:01 P = 1.3 × 10(-7) and DQB1*06:02 P = 6.1 × 10(-8)). Targeted RNA-sequencing of the HLA locus identified 21 genes differentially expressed between fibrotic and control lung tissue (Q < 0.001), many of which are involved in immune and inflammatory response regulation. In addition, the putative risk alleles, DRB1*15:01 and DQB1*06:02, are associated with expression of the DQB1 gene among fIIP cases (Q < 1 × 10(-16)). CONCLUSIONS We have identified a genome-wide significant association between the HLA region and fIIP. Two HLA alleles are associated with fIIP and affect expression of HLA genes in lung tissue, indicating that the potential genetic risk due to HLA alleles may involve gene regulation in addition to altered protein structure. 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We identified 19 shared genome segments (posterior probability > 90%), 15 with asthma and 4 with pulmonary fibrosis (Figure 4). For asthma, 7 of 15 overlapping segments did not reach genome-wide significance in COPD. In the 15 genome segments overlapping between COPD and asthma, in 4 of the segments (variants near LOC401242, TSLP, C11orf30, and IL33) the overlap is explained by a single variant with posterior probability > 90%. Two genome segments (near DSP and ZKSCAN1) in the COPD and pulmonary fibrosis overlap, are explain by a single variant with posterior probability > 90%. For all six overlapping associations explained by a single variant (4 for the COPD/asthma overlap and 2 for the COPD/pulmonary fibrosis overlap), the variant for COPD and either asthma or pulmonary fibrosis had an opposite direction of effect (i.e., increasing risk of COPD but protective for IPF and asthma).Phenotypic effects of known and novel associations for COPDTo characterize the phenotypic effects of 82 genome-wide significant loci, we performed a phenome-wide association analysis within the deeply phenotyped COPDGene studyADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.3109/15412550903499522","ISBN":"1541-2563 (Electronic)\r1541-2563 (Linking)","PMID":"20214461","abstract":"BACKGROUND: COPDGene is a multicenter observational study designed to identify genetic factors associated with COPD. It will also characterize chest CT phenotypes in COPD subjects, including assessment of emphysema, gas trapping, and airway wall thickening. Finally, subtypes of COPD based on these phenotypes will be used in a comprehensive genome-wide study to identify COPD susceptibility genes. METHODS/RESULTS: COPDGene will enroll 10,000 smokers with and without COPD across the GOLD stages. Both Non-Hispanic white and African-American subjects are included in the cohort. Inspiratory and expiratory chest CT scans will be obtained on all participants. In addition to the cross-sectional enrollment process, these subjects will be followed regularly for longitudinal studies. A genome-wide association study (GWAS) will be done on an initial group of 4000 subjects to identify genetic variants associated with case-control status and several quantitative phenotypes related to COPD. The initial findings will be verified in an additional 2000 COPD cases and 2000 smoking control subjects, and further validation association studies will be carried out. CONCLUSIONS: COPDGene will provide important new information about genetic factors in COPD, and will characterize the disease process using high resolution CT scans. Understanding genetic factors and CT phenotypes that define COPD will potentially permit earlier diagnosis of this disease and may lead to the development of treatments to modify progression.","author":[{"dropping-particle":"","family":"Regan","given":"E A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hokanson","given":"J E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Murphy","given":"J R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Make","given":"B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lynch","given":"D A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beaty","given":"T H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Curran-Everett","given":"D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"E K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crapo","given":"J D","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"COPD","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2010"]]},"note":"Regan, Elizabeth A\nHokanson, John E\nMurphy, James R\nMake, Barry\nLynch, David A\nBeaty, Terri H\nCurran-Everett, Douglas\nSilverman, Edwin K\nCrapo, James D\neng\nU01 HL089856-01A1/HL/NHLBI NIH HHS/\nU01 HL089897/HL/NHLBI NIH HHS/\nU01 HL089856/HL/NHLBI NIH HHS/\nU01HL089897/HL/NHLBI NIH HHS/\nU01 HL089897-01A1/HL/NHLBI NIH HHS/\nU01HL089856/HL/NHLBI NIH HHS/\nResearch Support, N.I.H., Extramural\nResearch Support, Non-U.S. Gov't\nEngland\n2010/03/11 06:00\nCOPD. 2010 Feb;7(1):32-43. doi: 10.3109/15412550903499522.","page":"32-43","title":"Genetic epidemiology of COPD (COPDGene) study design","type":"article-journal","volume":"7"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵⁵","plainTextFormattedCitation":"55","previouslyFormattedCitation":"⁵⁴"},"properties":{"noteIndex":0},"schema":""}55. We tested the overall structure of associated phenotypes by using hierarchical clustering across scaled Z scores of associations. We identified two clusters of associated variants (Supplementary Figure 6). These two clusters could be differentiated using 14 features including computed tomography (CT) subtypes, CT-derived quantitative emphysema and airway phenotypes, intensity and status of smoking, diffusing capacity of carbon monoxide, asthma, and inflammatory biomarkers. We further performed clustering limited to imaging features. We found two clusters of associations, differentiating by quantitative emphysema and gas trapping (Figure 3). We also examined all genome-wide significant loci in the NHGRI-EBI GWAS CatalogADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/nar/gkw1133","ISSN":"1362-4962","PMID":"27899670","abstract":"The NHGRI-EBI GWAS Catalog has provided data from published genome-wide association studies since 2008. 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Many variants associated with anthropometric measures including height and body mass index (BMI), various measurements on blood cells, and cancers (breast and prostate). Several variants also associated with comorbidities of COPD: atherosclerotic diseases (coronary artery disease and peripheral arterial disease), diabetes mellitus, bone density.Drug repositioning using drug-gene expression signaturesGWAS is also useful for identifying drug targets either at individual geneADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3787","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","ISSN":"1546-1718","PMID":"28166213","abstract":"Chronic obstructive pulmonary disease (COPD) is characterized by reduced lung function and is the third leading cause of death globally. 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Marjo-Riitta R; Wielscher, Matthias; James, Alan L; Hui, Jennie; Wareham, Nicholas J; Zhao, Jing Hua; Wilson, James F; Joshi, Peter K; Stubbe, Beate; Rawal, Rajesh; Schulz, Holger; Imboden, Medea; Probst-Hensch, Nicole M; Karrasch, Stefan; Gieger, Christian; Deary, Ian J; Harris, Sarah E; Marten, Jonathan; Rudan, Igor; Enroth, Stefan; Gyllensten, Ulf; Kerr, Shona M; Polasek, Ozren; Kahonen, M; Surakka, Ida; Vitart, Veronique; Hayward, Caroline; Lehtimaki, T; Raitakari, Olli T; Evans, David M; Henderson, A John; Pennell, Craig E; Wang, Carol A; Sly, Peter D; Wan, Emily S; Busch, Robert; Hobbs, Brian D; Litonjua, Augusto A; Sparrow, David W; Gulsvik, Amund; Bakke, Per S; Crapo, James D; Beaty, Terri H; Hansel, Nadia N; Mathias, Rasika A; Ruczinski, Ingo; Barnes, Kathleen C; Bosse, Y; Joubert, Philippe; van den Berge, Maarten; Brandsma, Corry-Anke A; Pare, P D; Sin, Don D; Nickle, David C; Hao, Ke; Gottesman, Omri; Dewey, Frederick E; Bruse, Shannon E; Carey, David J; Kirchner, H Lester; Geisinger-Regeneron Discov, E H R Collaboration; Jonsson, Stefan; Thorleifsson, Gudmar; Jonsdottir, Ingileif; Gislason, Thorarinn; Stefansson, Kari; Schurmann, Claudia; Nadkarni, Girish; Bottinger, Erwin P; Loos, Ruth J F; Walters, Robin G; Chen, Zhengming; Millwood, Iona Y; Vaucher, Julien; Kurmi, Om P; Li, Liming; Hansell, Anna L; Brightling, Chris; Zeggini, Eleftheria; Cho, Michael H; Silverman, Edwin K; Sayers, Ian; Trynka, Gosia; Morris, Andrew P; Strachan, David P; Hall, Ian P; Tobin, Martin D; Understanding Society Scientific Group; Campbell, Archie; Porteous, David J; Jarvelin, Marjo-Riitta R; Wielscher, Matthias; James, Alan L; Hui, Jennie; Wareham, Nicholas J; Zhao, Jing Hua; Wilson, James F; Joshi, Peter K; Stubbe, Beate; Rawal, Rajesh; Schulz, Holger; Imboden, Medea; Probst-Hensch, Nicole M; Karrasch, Stefan; Gieger, Christian; Deary, Ian J; Harris, Sarah E; Marten, Jonathan; Rudan, Igor; Enroth, Stefan; Gyllensten, Ulf; Kerr, Shona M; Polasek, Ozren; K?h?nen, Mika; Surakka, Ida; Vitart, Veronique; Hayward, Caroline; Lehtim?ki, Terho; Raitakari, Olli T; Evans, David M; Henderson, A John; Pennell, Craig E; Wang, Carol A; Sly, Peter D; Wan, Emily S; Busch, Robert; Hobbs, Brian D; Litonjua, Augusto A; Sparrow, David W; Gulsvik, Amund; Bakke, Per S; Crapo, James D; Beaty, Terri H; Hansel, Nadia N; Mathias, Rasika A; Ruczinski, Ingo; Barnes, Kathleen C; Bossé, Yohan; Joubert, Philippe; van den Berge, Maarten; Brandsma, Corry-Anke A; Paré, Peter D; Sin, Don D; Nickle, David C; Hao, Ke; Gottesman, Omri; Dewey, Frederick E; Bruse, Shannon E; Carey, David J; Kirchner, H Lester; Geisinger-Regeneron DiscovEHR Collaboration; Jonsson, Stefan; Thorleifsson, Gudmar; Jonsdottir, Ingileif; Gislason, Thorarinn; Stefansson, Kari; Schurmann, Claudia; Nadkarni, Girish; Bottinger, Erwin P; Loos, Ruth J F; Walters, Robin G; Chen, Zhengming; Millwood, Iona Y; Vaucher, Julien; Kurmi, Om P; Li, Liming; Hansell, Anna L; Brightling, Chris; Zeggini, Eleftheria; Cho, Michael H; Silverman, Edwin K; Sayers, Ian; Trynka, Gosia; Morris, Andrew P; Strachan, David P; Hall, Ian P; Tobin, Martin D)\n\nFrom Duplicate 2 (Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets - Wain, L V; Shrine, N; Artigas, M S; Erzurumluoglu, A M; Noyvert, B; Bossini-Castillo, L; Obeidat, M; Henry, A P; Portelli, M A; Hall, R J; Billington, C K; Rimington, T L; Fenech, A G; John, C; Blake, T; Jackson, V E; Allen, R J; Prins, B P; Understanding Society Scientific, Group; Campbell, A; Porteous, D J; Jarvelin, M R; Wielscher, M; James, A L; Hui, J; Wareham, N J; Zhao, J H; Wilson, J F; Joshi, P K; Stubbe, B; Rawal, R; Schulz, H; Imboden, M; Probst-Hensch, N M; Karrasch, S; Gieger, C; Deary, I J; Harris, S E; Marten, J; Rudan, I; Enroth, S; Gyllensten, U; Kerr, S M; Polasek, O; Kahonen, M; Surakka, I; Vitart, V; Hayward, C; Lehtimaki, T; Raitakari, O T; Evans, D M; Henderson, A J; Pennell, C E; Wang, C A; Sly, P D; Wan, E S; Busch, R; Hobbs, B D; Litonjua, A A; Sparrow, D W; Gulsvik, A; Bakke, P S; Crapo, J D; Beaty, T H; Hansel, N N; Mathias, R A; Ruczinski, I; Barnes, K C; Bosse, Y; Joubert, P; van den Berge, M; Brandsma, C A; Pare, P D; Sin, D D; Nickle, D C; Hao, K; Gottesman, O; Dewey, F E; Bruse, S E; Carey, D J; Kirchner, H L; Geisinger-Regeneron Discov, E H R Collaboration; Jonsson, S; Thorleifsson, G; Jonsdottir, I; Gislason, T; Stefansson, K; Schurmann, C; Nadkarni, G; Bottinger, E P; Loos, R J; Walters, R G; Chen, Z; Millwood, I Y; Vaucher, J; Kurmi, O P; Li, L; Hansell, A L; Brightling, C; Zeggini, E; Cho, M H; Silverman, E K; Sayers, I; Trynka, G; Morris, A P; Strachan, D P; Hall, I P; Tobin, M D)\n\nWain, Louise V\nShrine, Nick\nArtigas, Maria Soler\nErzurumluoglu, A Mesut\nNoyvert, Boris\nBossini-Castillo, Lara\nObeidat, Ma'en\nHenry, Amanda P\nPortelli, Michael A\nHall, Robert J\nBillington, Charlotte K\nRimington, Tracy L\nFenech, Anthony G\nJohn, Catherine\nBlake, Tineka\nJackson, Victoria E\nAllen, Richard J\nPrins, Bram P\nCampbell, Archie\nPorteous, David J\nJarvelin, Marjo-Riitta\nWielscher, Matthias\nJames, Alan L\nHui, Jennie\nWareham, Nicholas J\nZhao, Jing Hua\nWilson, James F\nJoshi, Peter K\nStubbe, Beate\nRawal, Rajesh\nSchulz, Holger\nImboden, Medea\nProbst-Hensch, Nicole M\nKarrasch, Stefan\nGieger, Christian\nDeary, Ian J\nHarris, Sarah E\nMarten, Jonathan\nRudan, Igor\nEnroth, Stefan\nGyllensten, Ulf\nKerr, Shona M\nPolasek, Ozren\nKahonen, Mika\nSurakka, Ida\nVitart, Veronique\nHayward, Caroline\nLehtimaki, Terho\nRaitakari, Olli T\nEvans, David M\nHenderson, A John\nPennell, Craig E\nWang, Carol A\nSly, Peter D\nWan, Emily S\nBusch, Robert\nHobbs, Brian D\nLitonjua, Augusto A\nSparrow, David W\nGulsvik, Amund\nBakke, Per S\nCrapo, James D\nBeaty, Terri H\nHansel, Nadia N\nMathias, Rasika A\nRuczinski, Ingo\nBarnes, Kathleen C\nBosse, Yohan\nJoubert, Philippe\nvan den Berge, Maarten\nBrandsma, Corry-Anke\nPare, Peter D\nSin, Don D\nNickle, David C\nHao, Ke\nGottesman, Omri\nDewey, Frederick E\nBruse, Shannon E\nCarey, David J\nKirchner, H Lester\nJonsson, Stefan\nThorleifsson, Gudmar\nJonsdottir, Ingileif\nGislason, Thorarinn\nStefansson, Kari\nSchurmann, Claudia\nNadkarni, Girish\nBottinger, Erwin P\nLoos, Ruth J F\nWalters, Robin G\nChen, Zhengming\nMillwood, Iona Y\nVaucher, Julien\nKurmi, Om P\nLi, Liming\nHansell, Anna L\nBrightling, Chris\nZeggini, Eleftheria\nCho, Michael H\nSilverman, Edwin K\nSayers, Ian\nTrynka, Gosia\nMorris, Andrew P\nStrachan, David P\nHall, Ian P\nTobin, Martin D\neng\nMR/K026992/1/Medical Research Council/United Kingdom\nMC_UU_12013/4/Medical Research Council/United Kingdom\nCZD/16/6/4/Chief Scientist Office/United Kingdom\nMC_PC_U127561128/Medical Research Council/United Kingdom\nG0902313/Medical Research Council/United Kingdom\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):416-425. doi: 10.1038/ng.3787. Epub 2017 Feb 6.","page":"416-425","title":"Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets","type":"article-journal","volume":"49"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/nbt.2151","ISSN":"1546-1696","PMID":"22491277","author":[{"dropping-particle":"","family":"Sanseau","given":"Philippe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Agarwal","given":"Pankaj","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barnes","given":"Michael R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pastinen","given":"Tomi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Richards","given":"J Brent","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cardon","given":"Lon R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mooser","given":"Vincent","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature biotechnology","id":"ITEM-2","issue":"4","issued":{"date-parts":[["2012","4","10"]]},"page":"317-20","title":"Use of genome-wide association studies for drug repositioning.","type":"article-journal","volume":"30"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1038/mp.2015.28","ISSN":"1476-5578","PMID":"25869805","abstract":"The Psychiatric Genomics Consortium-Schizophrenia Workgroup (PGC-SCZ) has recently published a genomewide association study (GWAS) identifying >100 genetic loci, encompassing a total of 341 protein-coding genes, attaining genomewide significance for susceptibility to schizophrenia. Given the extremely long time (12-15 years) and expense (>$1 billion) associated with the development of novel drug targets, repurposing of drugs with known and validated targets may be the most expeditious path toward deriving clinical utility from these GWAS findings. In the present study, we examined all genes within loci implicated by the PGC-SCZ GWAS against databases of targets of both approved and registered pharmaceutical compounds. We identified 20 potential schizophrenia susceptibility genes that encode proteins that are the targets of approved drugs. Of these, we prioritized genes/targets that are of clear neuropsychiatric interest and that are also sole members of the linkage disequilibrium block surrounding a PGC-SCZ GWAS hit. In addition to DRD2, 5 genes meet these criteria: CACNA1C, CACNB2, CACNA1I, GRIN2A and HCN1. An additional 20 genes coding for proteins that are the targets of drugs in registered clinical trials, but without approved indications, were also identified. 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Recent approaches to drug repositioning use high-throughput experimental approaches to assess a compound's potential therapeutic qualities. Here, we present a systematic computational approach to predict novel therapeutic indications on the basis of comprehensive testing of molecular signatures in drug-disease pairs. We integrated gene expression measurements from 100 diseases and gene expression measurements on 164 drug compounds, yielding predicted therapeutic potentials for these drugs. We recovered many known drug and disease relationships using computationally derived therapeutic potentials and also predict many new indications for these 164 drugs. We experimentally validated a prediction for the antiulcer drug cimetidine as a candidate therapeutic in the treatment of lung adenocarcinoma, and demonstrate its efficacy both in vitro and in vivo using mouse xenograft models. 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We identified seven compounds with an opposing connectivity score >= 90%: leu-enkephalin (an opioid receptor agonist), huperizine-a (an acetylcholinesterase inhibitor), periplocymarin (an apoptosis stimulant), PAC-1 (a caspase activator), TER-14687 (an inhibitor of translocation of PKCq in T cells), vincristine (a tubulin inhibitor), and terreic-acid (a Bruton's tyrosine kinase (BTK) inhibitor).DiscussionGenetic factors play an important role in COPD susceptibility; however, the majority of this genetic risk remains unexplained. Identifying new loci may help identify important disease pathways and also may help further explain the clinical heterogeneity seen in COPD. We examined genetic risk of COPD in a genome-wide association study including a total of 35,164 cases and 221,903 controls. We identified 82 genome-wide significant loci for COPD, including 16 previously identified in genome-wide association studies of COPD and additional 31 in population-based lung function, and 35 loci not previously described for COPD or lung function, of which 13 replicated in an independent study of population-based lung function.Our study helps support the role of early life events in the risk of COPD. We identified many sources of evidence supporting a role of developmental processes in the pathogenesis of COPD. The first evidence came from gene set enrichment analysis, identifying lung morphogenesis and lung alveolar development gene sets. We identified the involvement of various pathways including the canonical Wnt receptor signaling pathwayADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1164/rccm.201605-0904OC","ISSN":"1535-4970","PMID":"28245136","abstract":"RATIONALE Chronic obstructive pulmonary disease (COPD), in particular emphysema, is characterized by loss of parenchymal alveolar tissue and impaired tissue repair. Wingless and INT-1 (WNT)/β-catenin signaling is reduced in COPD; however, the mechanisms thereof, specifically the role of the frizzled (FZD) family of WNT receptors, remain unexplored. OBJECTIVES To identify and functionally characterize specific FZD receptors that control downstream WNT signaling in impaired lung repair in COPD. METHODS FZD expression was analyzed in lung homogenates and alveolar epithelial type II (ATII) cells of never-smokers, smokers, patients with COPD, and two experimental COPD models by quantitative reverse transcriptase-polymerase chain reaction, immunoblotting, and immunofluorescence. The functional effects of cigarette smoke on FZD4, WNT/β-catenin signaling, and elastogenic components were investigated in primary ATII cells in vitro and in three-dimensional lung tissue cultures ex vivo. Gain- and loss-of-function approaches were applied to determine the effects of FZD4 signaling on alveolar epithelial cell wound healing and repair, as well as on expression of elastogenic components. MEASUREMENTS AND MAIN RESULTS FZD4 expression was reduced in human and experimental COPD lung tissues as well as in primary human ATII cells from patients with COPD. Cigarette smoke exposure down-regulated FZD4 expression in vitro and in vivo, along with reduced WNT/β-catenin activity. Inhibition of FZD4 decreased WNT/β-catenin-driven epithelial cell proliferation and wound closure, and it interfered with ATII-to-ATI cell transdifferentiation and organoid formation, which were augmented by FZD4 overexpression. Moreover, FZD4 restoration by overexpression or pharmacological induction led to induction of WNT/β-catenin signaling and expression of elastogenic components in three-dimensional lung tissue cultures ex vivo. CONCLUSIONS Reduced FZD4 expression in COPD contributes to impaired alveolar repair capacity.","author":[{"dropping-particle":"","family":"Skronska-Wasek","given":"Wioletta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mutze","given":"Kathrin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Baarsma","given":"Hoeke A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bracke","given":"Ken R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alsafadi","given":"Hani N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lehmann","given":"Mareike","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Costa","given":"Rita","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stornaiuolo","given":"Mariano","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Novellino","given":"Ettore","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brusselle","given":"Guy G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wagner","given":"Darcy E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yildirim","given":"Ali ?","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"K?nigshoff","given":"Melanie","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of respiratory and critical care medicine","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2017","7","15"]]},"page":"172-185","title":"Reduced Frizzled Receptor 4 Expression Prevents WNT/β-Catenin-driven Alveolar Lung Repair in Chronic Obstructive Pulmonary Disease.","type":"article-journal","volume":"196"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1186/s12931-018-0744-9","ISSN":"1465-993X","PMID":"29566699","abstract":"BACKGROUND Genome-wide association studies have identified several genetic risk loci for severe chronic obstructive pulmonary disease (COPD) and emphysema. However, these studies do not fully explain disease heritability and in most cases, fail to implicate specific genes. Integrative methods that combine gene expression data with GWAS can provide more power in discovering disease-associated genes and give mechanistic insight into regulated genes. METHODS We applied a recently described method that imputes gene expression using reference transcriptome data to genome-wide association studies for two phenotypes (severe COPD and quantitative emphysema) and blood and lung tissue gene expression datasets. We further tested the potential causality of individual genes using multi-variant colocalization. RESULTS We identified seven genes significantly associated with severe COPD, and five genes significantly associated with quantitative emphysema in whole blood or lung. We validated results in independent transcriptome databases and confirmed colocalization signals for PSMA4, EGLN2, WNT3, DCBLD1, and LILRA3. Three of these genes were not located within previously reported GWAS loci for either phenotype. We also identified genetically driven pathways, including those related to immune regulation. CONCLUSIONS An integrative analysis of GWAS and gene expression identified novel associations with severe COPD and quantitative emphysema, and also suggested disease-associated genes in known COPD susceptibility loci. TRIAL REGISTRATION NCT00608764 , Registry: , Date of Enrollment of First Participant: November 2007, Date Registered: January 28, 2008 (retrospectively registered); NCT00292552 , Registry: , Date of Enrollment of First Participant: December 2005, Date Registered: February 14, 2006 (retrospectively registered).","author":[{"dropping-particle":"","family":"Sakornsakolpat","given":"Phuwanat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morrow","given":"Jarrett D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Castaldi","given":"Peter J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hersh","given":"Craig P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bossé","given":"Yohan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"Edwin K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Manichaikul","given":"Ani","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Respiratory research","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2018","3","22"]]},"page":"46","title":"Integrative genomics identifies new genes associated with severe COPD and emphysema.","type":"article-journal","volume":"19"},"uris":[""]}],"mendeley":{"formattedCitation":"^61,62","plainTextFormattedCitation":"61,62","previouslyFormattedCitation":"^60,61"},"properties":{"noteIndex":0},"schema":""}61,62, MAPK cascade, Ras protein signal transduction, and the nerve growth factor receptor signaling pathway. The second evidence emerged from the findings of importance of gene regulation at fetal stages. We identified enrichment of heritability in epigenomic marks of various fetal tissues, while fetal lung showing strongest signals. Lastly, we identified alveolar type 2 (AT2) cell, a regenerative cell in lung with surfactant production, as a putative target cell type for COPD. This finding could support the lung ageing hypothesisADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S2213-2600(18)30060-2","ISSN":"2213-2619","PMID":"29496484","author":[{"dropping-particle":"","family":"Agustí","given":"Alvar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Faner","given":"Rosa","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet. Respiratory medicine","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2018"]]},"page":"324-326","title":"COPD beyond smoking: new paradigm, novel opportunities.","type":"article-journal","volume":"6"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶³","plainTextFormattedCitation":"63","previouslyFormattedCitation":"⁶²"},"properties":{"noteIndex":0},"schema":""}63 which considers the suboptimal response to injury to noxious stimuli such as cigarette smoking, and recent findings on the regenerative potential of AT2 cellsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/nature25786","ISSN":"1476-4687","PMID":"29489752","abstract":"Functional tissue regeneration is required for the restoration of normal organ homeostasis after severe injury. Some organs, such as the intestine, harbour active stem cells throughout homeostasis and regeneration; more quiescent organs, such as the lung, often contain facultative progenitor cells that are recruited after injury to participate in regeneration. Here we show that a Wnt-responsive alveolar epithelial progenitor (AEP) lineage within the alveolar type 2 cell population acts as a major facultative progenitor cell in the distal lung. AEPs are a stable lineage during alveolar homeostasis but expand rapidly to regenerate a large proportion of the alveolar epithelium after acute lung injury. AEPs exhibit a distinct transcriptome, epigenome and functional phenotype and respond specifically to Wnt and Fgf signalling. In contrast to other proposed lung progenitor cells, human AEPs can be directly isolated by expression of the conserved cell surface marker TM4SF1, and act as functional human alveolar epithelial progenitor cells in 3D organoids. Our results identify the AEP lineage as an evolutionarily conserved alveolar progenitor that represents a new target for human lung regeneration strategies.","author":[{"dropping-particle":"","family":"Zacharias","given":"William J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frank","given":"David B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zepp","given":"Jarod A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morley","given":"Michael P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alkhaleel","given":"Farrah A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kong","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Su","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cantu","given":"Edward","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morrisey","given":"Edward E","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-1","issue":"7695","issued":{"date-parts":[["2018","3","8"]]},"page":"251-255","title":"Regeneration of the lung alveolus by an evolutionarily conserved epithelial progenitor.","type":"article-journal","volume":"555"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶⁴","plainTextFormattedCitation":"64","previouslyFormattedCitation":"⁶³"},"properties":{"noteIndex":0},"schema":""}64. Our findings are consistent with recent epidemiologic studies demonstrating that a substantial portion of the risk of COPD may be develop in early life: genetic findings may set initial lung functionADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S2213-2600(18)30100-0","ISSN":"2213-2619","PMID":"29628376","abstract":"BACKGROUND Lifetime lung function is related to quality of life and longevity. Over the lifespan, individuals follow different lung function trajectories. Identification of these trajectories, their determinants, and outcomes is important, but no study has done this beyond the fourth decade. METHODS We used six waves of the Tasmanian Longitudinal Health Study (TAHS) to model lung function trajectories measured at 7, 13, 18, 45, 50, and 53 years. We analysed pre-bronchodilator FEV1 z-scores at the six timepoints using group-based trajectory modelling to identify distinct subgroups of individuals whose measurements followed a similar pattern over time. We related the trajectories identified to childhood factors and risk of chronic obstructive pulmonary disease (COPD) using logistic regression, and estimated population-attributable fractions of COPD. FINDINGS Of the 8583 participants in the original cohort, 2438 had at least two waves of lung function data at age 7 years and 53 years and comprised the study population. We identified six trajectories: early below average, accelerated decline (97 [4%] participants); persistently low (136 [6%] participants); early low, accelerated growth, normal decline (196 [8%] participants); persistently high (293 [12%] participants); below average (772 [32%] participants); and average (944 [39%] participants). The three trajectories early below average, accelerated decline; persistently low; and below average had increased risk of COPD at age 53 years compared with the average group (early below average, accelerated decline: odds ratio 35·0, 95% CI 19·5-64·0; persistently low: 9·5, 4·5-20·6; and below average: 3·7, 1·9-6·9). Early-life predictors of the three trajectories included childhood asthma, bronchitis, pneumonia, allergic rhinitis, eczema, parental asthma, and maternal smoking. Personal smoking and active adult asthma increased the impact of maternal smoking and childhood asthma, respectively, on the early below average, accelerated decline trajectory. INTERPRETATION We identified six potential FEV1 trajectories, two of which were novel. Three trajectories contributed 75% of COPD burden and were associated with modifiable early-life exposures whose impact was aggravated by adult factors. We postulate that reducing maternal smoking, encouraging immunisation, and avoiding personal smoking, especially in those with smoking parents or low childhood lung function, might minimise COPD risk. Clinicians and patients with …","author":[{"dropping-particle":"","family":"Bui","given":"Dinh S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lodge","given":"Caroline J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burgess","given":"John A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lowe","given":"Adrian J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perret","given":"Jennifer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bui","given":"Minh Q","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bowatte","given":"Gayan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gurrin","given":"Lyle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johns","given":"David P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thompson","given":"Bruce R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hamilton","given":"Garun S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frith","given":"Peter A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"James","given":"Alan L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thomas","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jarvis","given":"Deborah","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Svanes","given":"Cecilie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Russell","given":"Melissa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morrison","given":"Stephen C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Feather","given":"Iain","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Allen","given":"Katrina J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wood-Baker","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hopper","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Giles","given":"Graham G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abramson","given":"Michael J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Walters","given":"Eugene H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Matheson","given":"Melanie C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dharmage","given":"Shyamali C","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet. Respiratory medicine","id":"ITEM-1","issued":{"date-parts":[["2018","4","5"]]},"title":"Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶⁵","plainTextFormattedCitation":"65","previouslyFormattedCitation":"⁶⁴"},"properties":{"noteIndex":0},"schema":""}65 and patterns of growth and declineADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S2213-2600(18)30100-0","ISSN":"2213-2619","PMID":"29628376","abstract":"BACKGROUND Lifetime lung function is related to quality of life and longevity. Over the lifespan, individuals follow different lung function trajectories. Identification of these trajectories, their determinants, and outcomes is important, but no study has done this beyond the fourth decade. METHODS We used six waves of the Tasmanian Longitudinal Health Study (TAHS) to model lung function trajectories measured at 7, 13, 18, 45, 50, and 53 years. We analysed pre-bronchodilator FEV1 z-scores at the six timepoints using group-based trajectory modelling to identify distinct subgroups of individuals whose measurements followed a similar pattern over time. We related the trajectories identified to childhood factors and risk of chronic obstructive pulmonary disease (COPD) using logistic regression, and estimated population-attributable fractions of COPD. FINDINGS Of the 8583 participants in the original cohort, 2438 had at least two waves of lung function data at age 7 years and 53 years and comprised the study population. We identified six trajectories: early below average, accelerated decline (97 [4%] participants); persistently low (136 [6%] participants); early low, accelerated growth, normal decline (196 [8%] participants); persistently high (293 [12%] participants); below average (772 [32%] participants); and average (944 [39%] participants). The three trajectories early below average, accelerated decline; persistently low; and below average had increased risk of COPD at age 53 years compared with the average group (early below average, accelerated decline: odds ratio 35·0, 95% CI 19·5-64·0; persistently low: 9·5, 4·5-20·6; and below average: 3·7, 1·9-6·9). Early-life predictors of the three trajectories included childhood asthma, bronchitis, pneumonia, allergic rhinitis, eczema, parental asthma, and maternal smoking. Personal smoking and active adult asthma increased the impact of maternal smoking and childhood asthma, respectively, on the early below average, accelerated decline trajectory. INTERPRETATION We identified six potential FEV1 trajectories, two of which were novel. Three trajectories contributed 75% of COPD burden and were associated with modifiable early-life exposures whose impact was aggravated by adult factors. We postulate that reducing maternal smoking, encouraging immunisation, and avoiding personal smoking, especially in those with smoking parents or low childhood lung function, might minimise COPD risk. Clinicians and patients with …","author":[{"dropping-particle":"","family":"Bui","given":"Dinh S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lodge","given":"Caroline J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burgess","given":"John A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lowe","given":"Adrian J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Perret","given":"Jennifer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bui","given":"Minh Q","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bowatte","given":"Gayan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gurrin","given":"Lyle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johns","given":"David P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thompson","given":"Bruce R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hamilton","given":"Garun S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Frith","given":"Peter A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"James","given":"Alan L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thomas","given":"Paul S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jarvis","given":"Deborah","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Svanes","given":"Cecilie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Russell","given":"Melissa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morrison","given":"Stephen C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Feather","given":"Iain","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Allen","given":"Katrina J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wood-Baker","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hopper","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Giles","given":"Graham G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abramson","given":"Michael J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Walters","given":"Eugene H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Matheson","given":"Melanie C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dharmage","given":"Shyamali C","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet. Respiratory medicine","id":"ITEM-1","issued":{"date-parts":[["2018","4","5"]]},"title":"Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life.","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1056/NEJMoa1513737","ISSN":"1533-4406","PMID":"27168434","abstract":"BACKGROUND Tracking longitudinal measurements of growth and decline in lung function in patients with persistent childhood asthma may reveal links between asthma and subsequent chronic airflow obstruction. METHODS We classified children with asthma according to four characteristic patterns of lung-function growth and decline on the basis of graphs showing forced expiratory volume in 1 second (FEV1), representing spirometric measurements performed from childhood into adulthood. Risk factors associated with abnormal patterns were also examined. To define normal values, we used FEV1 values from participants in the National Health and Nutrition Examination Survey who did not have asthma. RESULTS Of the 684 study participants, 170 (25%) had a normal pattern of lung-function growth without early decline, and 514 (75%) had abnormal patterns: 176 (26%) had reduced growth and an early decline, 160 (23%) had reduced growth only, and 178 (26%) had normal growth and an early decline. Lower baseline values for FEV1, smaller bronchodilator response, airway hyperresponsiveness at baseline, and male sex were associated with reduced growth (P<0.001 for all comparisons). At the last spirometric measurement (mean [±SD] age, 26.0±1.8 years), 73 participants (11%) met Global Initiative for Chronic Obstructive Lung Disease spirometric criteria for lung-function impairment that was consistent with chronic obstructive pulmonary disease (COPD); these participants were more likely to have a reduced pattern of growth than a normal pattern (18% vs. 3%, P<0.001). CONCLUSIONS Childhood impairment of lung function and male sex were the most significant predictors of abnormal longitudinal patterns of lung-function growth and decline. Children with persistent asthma and reduced growth of lung function are at increased risk for fixed airflow obstruction and possibly COPD in early adulthood. (Funded by the Parker B. 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OBJECTIVES 1) To model trajectories using a data-driven approach applied to longitudinal data spanning adulthood in the Normative Aging Study (NAS), and 2) to apply these models to demographically similar subjects in the COPDGene Study with detailed phenotypic characterization including chest CT. METHODS We modeled lung function trajectories in 1,060 subjects in NAS with a median follow-up time of 29 years. We assigned 3,546 non-Hispanic white males in COPDGene to these trajectories for further analysis. We assessed phenotypic and genetic differences between trajectories and across age strata. MEASUREMENTS AND MAIN RESULTS We identified four trajectories in NAS with differing levels of maximum lung function and rate of decline. In COPDGene, 617 (17%) subjects were assigned to the lowest trajectory and had the greatest radiologic burden of disease (P<0.01); 1,283 (36%) subjects were assigned to a low trajectory with evidence of airways disease preceding emphysema on CT; 1,411 (40%) and 237 (7%) subjects were assigned to the remaining two trajectories and tended to have preserved lung function and negligible emphysema. The genetic contribution to these trajectories was as high as 83% (p=0.02), and membership in lower lung function trajectories was associated with greater parental histories of COPD, decreased exercise capacity, greater dyspnea, and more frequent COPD exacerbations. CONCLUSIONS Data-driven analysis identifies four lung function trajectories. Trajectory membership has a genetic basis and is associated with distinct lung structural abnormalities.","author":[{"dropping-particle":"","family":"Ross","given":"James C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Castaldi","given":"Peter J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hersh","given":"Craig P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rahaghi","given":"Farbod N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V","family":"Sánchez-Ferrero","given":"Gonzalo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Parker","given":"Margaret M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Litonjua","given":"Augusto A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sparrow","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dy","given":"Jennifer G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"Edwin K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Washko","given":"George R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"San José Estépar","given":"Raúl","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American journal of respiratory and critical care medicine","id":"ITEM-3","issued":{"date-parts":[["2018","4","19"]]},"title":"Longitudinal Modeling of Lung Function Trajectories in Smokers with and without COPD.","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"^65–67","plainTextFormattedCitation":"65–67","previouslyFormattedCitation":"^64–66"},"properties":{"noteIndex":0},"schema":""}65–67. In addition to genes directly related to lung development, we also identified many pathways related to supporting elements including mesenchyme development and extracellular matrix. For example, we identified ADAMTSL3 as a potential effector gene at a novel susceptibility locus. In addition to a role in development, this gene plays a role in cell-matrix interactions or in assembly of specific extracellular matricesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"0945-053X","PMID":"14667842","abstract":"The complete primary structure of ADAMTSL-3/punctin-2, a novel member of the family designated ADAMTSL (a disintegrin-like and metalloprotease domain with thrombospondin type I motifs-like), was determined by cDNA cloning from a human placenta library. The predicted open reading frame encodes a protein of 1690 amino acids that has considerable similarity to ADAMTSL-1/punctin-1. These multi-domain proteins lack both a protease domain and a disintegrin-like domain but are remarkably similar in their domain organization to the ADAMTS proteases, hence the name ADAMTS-like. Punctin-2 contains thrombospondin type 1 repeats (TSRs), a cysteine-rich domain and a cysteine-free spacer domain in the precise order in which they occur in the ADAMTS proteases. However, the number and organization of the TSRs in punctin-2 is unique with respect to the ADAMTS proteases. Punctin-2 contains 13 TSRs arranged in two arrays separated by a region containing three immunoglobulin-like repeats. Northern blot analysis of RNA from human adult tissues demonstrated that ADAMTSL3 is widely expressed, with highest expression in liver, kidney, heart and skeletal muscle, whereas it is expressed at low levels in mouse embryos. We characterized two punctin-2 polyclonal antisera. Using these and a monoclonal antibody to a C-terminal myc tag, we show that in transfected COS-7 cells, punctin-2 is expressed as a 210-kDa glycoprotein that is located in the extracellular matrix. The domain structure of punctin-2 and its matrix localization suggest that it might play a role in cell-matrix interactions or in assembly of specific extracellular matrices.","author":[{"dropping-particle":"","family":"Hall","given":"Nina G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klenotic","given":"Philip","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anand-Apte","given":"Bela","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Apte","given":"Suneel S","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Matrix biology : journal of the International Society for Matrix Biology","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2003","11"]]},"page":"501-10","title":"ADAMTSL-3/punctin-2, a novel glycoprotein in extracellular matrix related to the ADAMTS family of metalloproteases.","type":"article-journal","volume":"22"},"uris":[""]}],"mendeley":{"formattedCitation":"⁶⁸","plainTextFormattedCitation":"68","previouslyFormattedCitation":"⁶⁷"},"properties":{"noteIndex":0},"schema":""}68. Our study also supports additional pathways previously hypothesized to play a role in COPD, some of which have been previously identified in studies of lung functionADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.941","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","PMID":"21946350","abstract":"Pulmonary function measures reflect respiratory health and are used in the diagnosis of chronic obstructive pulmonary disease. We tested genome-wide association with forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity in 48,201 individuals of European ancestry with follow up of the top associations in up to an additional 46,411 individuals. We identified new regions showing association (combined P < 5 x 10(-8)) with pulmonary function in or near MFAP2, TGFB2, HDAC4, RARB, MECOM (also known as EVI1), SPATA9, ARMC2, NCR3, ZKSCAN3, CDC123, C10orf11, LRP1, CCDC38, MMP15, CFDP1 and KCNE2. 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Despite being the largest cytoskeleton, its structural composition has remained unknown. Here, we report a novel 220-kD protein, designated rootletin, found in the rootlets of ciliated cells. Recombinant rootletin forms detergent-insoluble filaments radiating from the centrioles and resembling rootlets found in vivo. An mAb widely used as a marker for vertebrate rootlets recognizes an epitope in rootletin. Rootletin has a globular head domain and a tail domain consisting of extended coiled-coil structures. Rootletin forms parallel in register homodimers and elongated higher order polymers mediated by the tail domain alone. The head domain may be required for targeting to the basal body and binding to a kinesin light chain. In retinal photoreceptors where rootlets appear particularly robust, rootlets extend from the basal bodies to the synaptic terminals and anchor ER membranes along their length. Our data indicate that rootlets are composed of homopolymeric rootletin protofilaments bundled into variably shaped thick filaments. Thus, rootletin is the long-sought structural component of the ciliary rootlet.","author":[{"dropping-particle":"","family":"Yang","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Liu","given":"Xiaoqing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yue","given":"Guohua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Adamian","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bulgakov","given":"Oleg","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Tiansen","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Journal of cell biology","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2002","11","11"]]},"page":"431-40","title":"Rootletin, a novel coiled-coil protein, is a structural component of the ciliary rootlet.","type":"article-journal","volume":"159"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷⁰","plainTextFormattedCitation":"70","previouslyFormattedCitation":"⁶⁹"},"properties":{"noteIndex":0},"schema":""}70, elastin-associated microfibrilsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"0021-9258","PMID":"3015971","abstract":"The major antigen derived from elastic fiber microfibrils was identified as a Mr = 31,000 glycoprotein, using immunoblotting and immunohistochemical techniques with antisera raised to \"reductive guanidine extracts\" of fetal bovine nuchal ligament, and to subfractions of these. A second, elastic fiber-derived, but unidentified, antigen of large molecular size (Mr greater than 200,000) was present in these extracts. Antisera raised to the purified 31-kDa glycoprotein were shown, by immunoelectron microscopy, to localize specifically to the elastin-associated microfibrils. Thus, the macro-molecule was called \"microfibril-associated glycoprotein\" or MAGP. MAGP is an acidic glycoprotein with a distinctive amino acid composition, being exceptionally rich in glutamic acid, rich in cystine, and low in glycine. MAGP was extractable from tissue homogenates using NaCl, urea, or guanidine hydrochloride solutions, only if a strong reducing agent was present. Thus, disulfide bonding is important for the strong association of MAGP with elastic fibers. Immunoblotting with anti-MAGP antiserum identified two additional reactive species, of Mr = 60,000 and Mr approximately 300,000, in tissue extracts. As only the 31-kDa species was detected in fibroblast culture medium, these additional species were probably aggregates, rather than precursors. MAGP did not react with antilysyl oxidase antiserum on immunoblots or by enzyme-linked immunosorbent assay. MAGP is the first macromolecule to have been established to be a constituent of elastin-associated microfibrils in both developing and mature elastic tissues.","author":[{"dropping-particle":"","family":"Gibson","given":"M A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hughes","given":"J L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fanning","given":"J C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cleary","given":"E G","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Journal of biological chemistry","id":"ITEM-1","issue":"24","issued":{"date-parts":[["1986","8","25"]]},"page":"11429-36","title":"The major antigen of elastin-associated microfibrils is a 31-kDa glycoprotein.","type":"article-journal","volume":"261"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷¹","plainTextFormattedCitation":"71","previouslyFormattedCitation":"⁷⁰"},"properties":{"noteIndex":0},"schema":""}71, and retinoic acid receptor betaADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1152/physiolgenomics.2000.4.1.51","ISSN":"1531-2267","PMID":"11074013","abstract":"Pulmonary alveoli are formed, in part, by subdivision (septation) of the gas-exchange saccules of the immature lung. Septation is developmentally regulated, and failure to septate at the appropriate time is not followed by delayed spontaneous septation. We report retinoic acid receptor (RAR) beta knockout mice exhibit premature septation; in addition, they form alveoli twice as fast as wild-type mice during the period of septation but at the same rate as wild-type mice thereafter. Consistent with the perinatal effect of RARbeta knockout, RARbeta agonist treatment of newborn rats impairs septation. These results 1) identify RARbeta as the first recognized endogenous signaling that inhibits septation, 2) demonstrate premature onset of septation may be induced, and 3) show the molecular signaling regulating alveolus formation differs during and after the period of septation. Suppressing perinatal RARbeta signaling by RARbeta antagonists may offer a novel, nonsurgical, means of preventing, or remediating, failed septation in prematurely born children.","author":[{"dropping-particle":"","family":"Massaro","given":"G D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Massaro","given":"D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chan","given":"W Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Clerch","given":"L B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghyselinck","given":"N","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chambon","given":"P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chandraratna","given":"R A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Physiological genomics","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2000","11","9"]]},"page":"51-7","title":"Retinoic acid receptor-beta: an endogenous inhibitor of the perinatal formation of pulmonary alveoli.","type":"article-journal","volume":"4"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷²","plainTextFormattedCitation":"72","previouslyFormattedCitation":"⁷¹"},"properties":{"noteIndex":0},"schema":""}72.We identified enrichment of epigenomic marks in lung tissue and smooth muscle (also identified in studies of lung functionADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S2213-2600(15)00283-0","ISBN":"2213-2619 (Electronic)\r2213-2600 (Linking)","PMID":"26423011","abstract":"BACKGROUND: Understanding the genetic basis of airflow obstruction and smoking behaviour is key to determining the pathophysiology of chronic obstructive pulmonary disease (COPD). We used UK Biobank data to study the genetic causes of smoking behaviour and lung health. METHODS: We sampled individuals of European ancestry from UK Biobank, from the middle and extremes of the forced expiratory volume in 1 s (FEV1) distribution among heavy smokers (mean 35 pack-years) and never smokers. We developed a custom array for UK Biobank to provide optimum genome-wide coverage of common and low-frequency variants, dense coverage of genomic regions already implicated in lung health and disease, and to assay rare coding variants relevant to the UK population. We investigated whether there were shared genetic causes between different phenotypes defined by extremes of FEV1. We also looked for novel variants associated with extremes of FEV1 and smoking behaviour and assessed regions of the genome that had already shown evidence for a role in lung health and disease. We set genome-wide significance at p<5 x 10(-8). FINDINGS: UK Biobank participants were recruited from March 15, 2006, to July 7, 2010. Sample selection for the UK BiLEVE study started on Nov 22, 2012, and was completed on Dec 20, 2012. We selected 50,008 unique samples: 10,002 individuals with low FEV1, 10,000 with average FEV1, and 5002 with high FEV1 from each of the heavy smoker and never smoker groups. We noted a substantial sharing of genetic causes of low FEV1 between heavy smokers and never smokers (p=2.29 x 10(-16)) and between individuals with and without doctor-diagnosed asthma (p=6.06 x 10(-11)). We discovered six novel genome-wide significant signals of association with extremes of FEV1, including signals at four novel loci (KANSL1, TSEN54, TET2, and RBM19/TBX5) and independent signals at two previously reported loci (NPNT and HLA-DQB1/HLA-DQA2). These variants also showed association with COPD, including in individuals with no history of smoking. The number of copies of a 150 kb region containing the 5' end of KANSL1, a gene that is important for epigenetic gene regulation, was associated with extremes of FEV1. We also discovered five new genome-wide significant signals for smoking behaviour, including a variant in NCAM1 (chromosome 11) and a variant on chromosome 2 (between TEX41 and PABPC1P2) that has a trans effect on expression of NCAM1 in brain tissue. 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V\nShrine, Nick\nMiller, Suzanne\nJackson, Victoria E\nNtalla, Ioanna\nSoler Artigas, Maria\nBillington, Charlotte K\nKheirallah, Abdul Kader\nAllen, Richard\nCook, James P\nProbert, Kelly\nObeidat, Ma'en\nBosse, Yohan\nHao, Ke\nPostma, Dirkje S\nPare, Peter D\nRamasamy, Adaikalavan\n(UKBEC)\nMagi, Reedik\nMihailov, Evelin\nReinmaa, Eva\nMelen, Erik\nO'Connell, Jared\nFrangou, Eleni\nDelaneau, Olivier\nFreeman, Colin\nPetkova, Desislava\nMcCarthy, Mark\nSayers, Ian\nDeloukas, Panos\nHubbard, Richard\nPavord, Ian\nHansell, Anna L\nThomson, Neil C\nZeggini, Eleftheria\nMorris, Andrew P\nMarchini, Jonathan\nStrachan, David P\nTobin, Martin D\nHall, Ian P\neng\n098017/Wellcome Trust/United Kingdom\nP30 CA016672/CA/NCI NIH HHS/\nG1000861/Medical Research Council/United Kingdom\n093707/Wellcome Trust/United Kingdom\nMC_PC_12010/Medical Research Council/United Kingdom\nMR/L01341X/1/Medical Research Council/United Kingdom\nG0501942/Medical Research Council/United Kingdom\nG0800759/Medical 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Epub 2015 Sep 27.","page":"769-781","title":"Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank","type":"article-journal","volume":"3"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷³","plainTextFormattedCitation":"73","previouslyFormattedCitation":"⁷²"},"properties":{"noteIndex":0},"schema":""}73); this latter association is driven by non-respiratory cell types, and suggests that more gene expression and regulatory mapping of both fetal and adult lung cells including in-depth characterization of cell lineages in the lung is needed to further characterize cell types affected by disease-associated variants. We identified many candidate genes at genome-wide significant loci. Combining five molecular datasets, we found 481 genes at 82 loci that were supported by at least one dataset, and 106 genes in at least two datasets. Among our novel findings are an association with the chitinase acidic (CHIA) gene at 1p13.3, which encodes a protein that degrades chitinADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/nar/gkv1189","ISSN":"1362-4962","PMID":"26553804","abstract":"The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.","author":[{"dropping-particle":"","family":"O'Leary","given":"Nuala A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wright","given":"Mathew W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brister","given":"J 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Its expression is specific to lungADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"0378-1119","PMID":"10548734","abstract":"Using differential display technique, we have isolated a novel human gene expressed specifically in the lung. Two forms of the gene, designated TSA1902, were transcribed by alternate mRNA splicing. The transcribed mRNAs, termed TSA1902-L and TSA1902-S, putatively encode proteins of 368 and 315 amino acids, respectively, which show high similarity to human chitotriosidase protein. The N-terminal region of TSA1902-L protein contains the conserved active site residues (DXXDXDXE) of the catalytic center of various chitinases which are essential for chitinase activity. The deduced protein sequence of TSA1902-S, however, does not possess this active site, with the N-terminal 54 amino acids present in TSA1902-L protein having been deleted. Both proteins lacked the secretory sequence of N-termini and, judging from the hydropathy profile, may be soluble proteins in the cytoplasm. Chromosomal mapping by radiation hybrid analysis localized this gene to the chromosome 1p13.1-p21.3.","author":[{"dropping-particle":"","family":"Saito","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ozaki","given":"K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fujiwara","given":"T","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nakamura","given":"Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tanigami","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Gene","id":"ITEM-1","issue":"2","issued":{"date-parts":[["1999","11","1"]]},"page":"325-31","title":"Isolation and mapping of a human lung-specific gene, TSA1902, encoding a novel chitinase family member.","type":"article-journal","volume":"239"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1074/mcp.M113.035600","ISSN":"1535-9484","PMID":"24309898","abstract":"Global classification of the human proteins with regards to spatial expression patterns across organs and tissues is important for studies of human biology and disease. Here, we used a quantitative transcriptomics analysis (RNA-Seq) to classify the tissue-specific expression of genes across a representative set of all major human organs and tissues and combined this analysis with antibody-based profiling of the same tissues. To present the data, we launch a new version of the Human Protein Atlas that integrates RNA and protein expression data corresponding to ～80% of the human protein-coding genes with access to the primary data for both the RNA and the protein analysis on an individual gene level. We present a classification of all human protein-coding genes with regards to tissue-specificity and spatial expression pattern. The integrative human expression map can be used as a starting point to explore the molecular constituents of the human body.","author":[{"dropping-particle":"","family":"Fagerberg","given":"Linn","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hallstr?m","given":"Bj?rn M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oksvold","given":"Per","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kampf","given":"Caroline","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Djureinovic","given":"Dijana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Odeberg","given":"Jacob","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Habuka","given":"Masato","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tahmasebpoor","given":"Simin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Danielsson","given":"Angelika","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Edlund","given":"Karolina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Asplund","given":"Anna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sj?stedt","given":"Evelina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lundberg","given":"Emma","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Szigyarto","given":"Cristina Al-Khalili","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Skogs","given":"Marie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Takanen","given":"Jenny Ottosson","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berling","given":"Holger","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tegel","given":"Hanna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mulder","given":"Jan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nilsson","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schwenk","given":"Jochen M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lindskog","given":"Cecilia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Danielsson","given":"Frida","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mardinoglu","given":"Adil","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sivertsson","given":"Asa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Feilitzen","given":"Kalle","non-dropping-particle":"von","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Forsberg","given":"Mattias","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zwahlen","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Olsson","given":"IngMarie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Navani","given":"Sanjay","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huss","given":"Mikael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nielsen","given":"Jens","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ponten","given":"Fredrik","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Uhlén","given":"Mathias","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Molecular & cellular proteomics : MCP","id":"ITEM-2","issue":"2","issued":{"date-parts":[["2014","2"]]},"page":"397-406","title":"Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics.","type":"article-journal","volume":"13"},"uris":[""]}],"mendeley":{"formattedCitation":"^75,76","plainTextFormattedCitation":"75,76","previouslyFormattedCitation":"^74,75"},"properties":{"noteIndex":0},"schema":""}75,76. Genetic variants in or around this gene were associated with baseline FEV1 and rate of FEV1 declineADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s00439-011-1127-1","ISSN":"1432-1203","PMID":"22200767","abstract":"Two primary chitinases have been identified in humans--acid mammalian chitinase (AMCase) and chitotriosidase (CHIT1). Mammalian chitinases have been observed to affect the host's immune response. The aim of this study was to test for association between genetic variation in the chitinases and phenotypes related to chronic obstructive pulmonary disease (COPD). Polymorphisms in the chitinase genes were selected based on previous associations with respiratory diseases. Polymorphisms that were associated with lung function level or rate of decline in the Lung Health Study (LHS) cohort were analyzed for association with COPD affection status in four other COPD case-control populations. Chitinase activity and protein levels were also related to genotypes. In the caucasian LHS population, the baseline forced expiratory volume in one second (FEV(1)) was significantly different between the AA and GG genotypic groups of the AMCase rs3818822 polymorphism. Subjects with the GG genotype had higher AMCase protein and chitinase activity compared with AA homozygotes. For CHIT1 rs2494303, a significant association was observed between rate of decline in FEV(1) and the different genotypes. In the African American LHS population, CHIT1 rs2494303 and AMCase G339T genotypes were associated with rate of decline in FEV(1). Although a significant effect of chitinase gene alleles was found on lung function level and decline in the LHS, we were unable to replicate the associations with COPD affection status in the other COPD study groups.","author":[{"dropping-particle":"","family":"Aminuddin","given":"F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Akhabir","given":"L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stefanowicz","given":"D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paré","given":"P D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Connett","given":"J E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anthonisen","given":"N R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V","family":"Fahy","given":"J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seibold","given":"M A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burchard","given":"E G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Eng","given":"C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gulsvik","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bakke","given":"P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"M H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Litonjua","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lomas","given":"D A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anderson","given":"W H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beaty","given":"T H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crapo","given":"J D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"E K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sandford","given":"A J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Human genetics","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2012","7"]]},"page":"1105-14","title":"Genetic association between human chitinases and lung function in COPD.","type":"article-journal","volume":"131"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷⁷","plainTextFormattedCitation":"77","previouslyFormattedCitation":"⁷⁶"},"properties":{"noteIndex":0},"schema":""}77, asthmaADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.rmed.2011.03.017","ISSN":"1532-3064","PMID":"21511453","abstract":"BACKGROUND Studies regarding the physiological role of acidic mammalian chitinase (AMCase) and the effects of its genetic variants on asthma have produced conflicting results. OBJECTIVES We aimed to determine the genetic variants in the AMCase gene that could regulate the gene expression and thus influence disease severity. METHODS Genetic variants of the AMCase gene were determined by sequencing of asthmatics and healthy controls in up to -1 kb in the promoter region and exon 1 and 2. In an association study, a population of asthmatic (n = 504) and healthy Turkish children (n = 188) were genotyped for the observed SNPs. A replication study was performed in a North American adult population of patients with mild (n = 317) and severe (n = 145) asthma. The functional properties of the insertion were determined by promoter reporter assay, electromobility shift assay and transcription factor ELISA experiments. RESULTS Of the identified SNPs, only a ten base pair insertion (CAATCTAGGC) in the 5'UTR region of exon 2 was significantly associated with lower FEV(1) (β = -14.63 SE = 6.241, P = 0.019) in Turkish children with asthma. However, in the adult population, the same insertion showed a trend toward higher FEV(1). The insertion was shown to have enhancer activity and the mutant probe possessing the insertion had higher binding affinity for the nuclear extracts. CONCLUSION Our study shows that a ten base pair insertion in the 5'UTR region of AMCase gene may modify gene expression and thus may affect the severity of asthma. However, its effects appear to be different in different populations.","author":[{"dropping-particle":"","family":"Birben","given":"Esra","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sackesen","given":"Cans?n","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kazani","given":"Shamsah","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tincer","given":"Gizem","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Karaaslan","given":"Cagatay","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Durgunsu","given":"Berna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gürsel","given":"Ihsan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wechsler","given":"Michael E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Israel","given":"Elliot","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kalayci","given":"Omer","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Respiratory medicine","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2011","8"]]},"page":"1160-9","title":"The effects of an insertion in the 5'UTR of the AMCase on gene expression and pulmonary functions.","type":"article-journal","volume":"105"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1016/j.jaci.2008.04.030","ISSN":"1097-6825","PMID":"18602573","abstract":"BACKGROUND In view of the hygiene hypothesis and the involvement of acidic mammalian chitinase (CHIA) in the effector responses of IL-13 with asthma, CHIA (GeneID-27159) is a potential asthma candidate gene. OBJECTIVE To investigate the association of CHIA polymorphisms with atopic asthma and serum total IgE levels. METHODS Twenty-one single nucleotide polymorphisms were identified by sequencing DNA of 60 individuals. On the basis of linkage disequilibrium, 6 polymorphisms were selected and genotyped in unrelated atopic patients with asthma (N = 270) and controls (N = 292) and an independent pediatric cohort (patients, 150; controls, 101). Electrophoretic mobility shift assay and reporter gene assays were also performed. RESULTS The rs3806448G/A promoter polymorphism showed significant association with atopic asthma (P(adult) = .00001 and P(pediatric) = .0002) and serum total IgE (P < .05). Also rs2282290G/A was associated with atopic asthma (P(adult) = .00009 and P(pediatric) = .00003), whereas the rs10494132C/T polymorphism was associated with serum total IgE in the patients (P < .05). We also showed that the promoter single nucleotide polymorphisms altered the transcriptional activity of CHIA promoter and the C to T substitution at rs10494132 abrogated the Octamer transcription factor-1 (Oct-1) binding site. CONCLUSION Our results establish a significant association of CHIA with atopic asthma and serum total IgE levels in the Indian population.","author":[{"dropping-particle":"","family":"Chatterjee","given":"Rajshekhar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Batra","given":"Jyotsna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Das","given":"Sudipta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sharma","given":"Surendra Kumar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghosh","given":"Balaram","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Journal of allergy and clinical immunology","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2008","7"]]},"page":"202-8, 208.e1-7","title":"Genetic association of acidic mammalian chitinase with atopic asthma and serum total IgE levels.","type":"article-journal","volume":"122"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1097/ACI.0b013e3283306533","ISSN":"1473-6322","PMID":"19644363","abstract":"PURPOSE OF REVIEW The present review provides an overview of the chitinase and chitinase-like proteins, chitotriosidase (CHIT1), YKL-40, and acid mammalian chitinase, and summarizes the genetic studies of asthma and immune-mediated diseases with polymorphisms in the genes encoding these proteins, CHIT1, CHI3L1, and CHIA, respectively. RECENT FINDINGS Polymorphisms in the CHIT1, CHIA, and CHI3L1 genes influence chitotriosidase enzyme activity, acid mammalian chitinase activity, and YKL-40 levels, respectively. Regulatory SNPs in CHI3L1 were also associated with asthma, atopy, and immune-mediated diseases, and nonsynonymous SNPs in CHIA were associated with asthma. No CHIT1 polymorphisms, including a common nonfunctional 24-bp duplication allele, have been associated with asthma. SUMMARY These genes represent novel asthma susceptibility genes. Variations in CHI3L1 and CHIA have been associated with asthma risk. Polymorphisms in CHIT1 have not yet been associated with asthma, but few studies have been reported. Given that chitotriosidase is the major chitinase in the airways and a common nonfunctional allele is present in many populations, additional studies of this gene are warranted. Lastly, studies of all three genes need to be conducted in populations of diverse ancestries.","author":[{"dropping-particle":"","family":"Ober","given":"Carole","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chupp","given":"Geoffrey L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Current opinion in allergy and clinical immunology","id":"ITEM-3","issue":"5","issued":{"date-parts":[["2009","10"]]},"page":"401-8","title":"The chitinase and chitinase-like proteins: a review of genetic and functional studies in asthma and immune-mediated diseases.","type":"article-journal","volume":"9"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1111/j.1399-3038.2010.01053.x","ISSN":"1399-3038","PMID":"20444155","abstract":"In the genetics of asthma, single genetic polymorphisms confer only a small individual risk factor. Haplotype-based association analyses, including joint analyses of several candidate genes, might therefore yield more convincing results than single-region statistics. We set out to test for joint influences of asthma genes previously identified in our study population that is acidic mammalian chitinase (AMCase), Toll-like receptor (TLR)-10, and the interleukins IL-4, IL-13, IL-8, and IL-15. In particular, we investigated whether haplotypes at two or three genes show stronger association with the trait than at a single gene alone. We genotyped 26 polymorphisms in 321 asthmatic children and 270 controls. Haplotype-based association analyses were performed by the program FAMHAP. Single-, two-, and three-gene analyses were conducted as well as conditional analyses for pairs of genes. In the two-region analyses, best evidence was found for a joint effect on asthma for AMCase and IL-4 (p(raw) < 5 x 10(-7)) as well as AMCase and IL-13 (p(raw) = 5 x 10(-7)). Besides, IL-13 and TLR-10 showed a stronger two-gene result (p(raw) = 0.001607) than the respective single-gene analyses. Conditional analyses yielded similar results for these two-gene combinations and also revealed mutual additional effects for IL-13 and IL-4 (p(stratified) = 0.014831 and 0.001525, respectively). The most significant results demonstrate a joint effect of AMCase with IL-4 or IL-13 on the trait. Furthermore, additional mutual effects were seen for AMCase and IL-4 as well as for TLR-10 and IL-13. The corresponding pathways might therefore be of particular importance in the genetics of asthma. Further studies are needed to elucidate the functional importance of these gene-gene effects and their precise role in asthma pathogenesis.","author":[{"dropping-particle":"","family":"Heinzmann","given":"Andrea","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brugger","given":"Markus","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bierbaum","given":"Sibylle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mailaparambil","given":"Beena","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V","family":"Kopp","given":"Matthias","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Strauch","given":"Konstantin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology","id":"ITEM-4","issue":"4 Pt 2","issued":{"date-parts":[["2010","6"]]},"page":"e679-86","title":"Joint influences of Acidic-Mammalian-Chitinase with Interleukin-4 and Toll-like receptor-10 with Interleukin-13 in the genetics of asthma.","type":"article-journal","volume":"21"},"uris":[""]}],"mendeley":{"formattedCitation":"^78–81","plainTextFormattedCitation":"78–81","previouslyFormattedCitation":"^77–80"},"properties":{"noteIndex":0},"schema":""}78–81, acid mammalian chitinase activityADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1097/ACI.0b013e3283306533","ISSN":"1473-6322","PMID":"19644363","abstract":"PURPOSE OF REVIEW The present review provides an overview of the chitinase and chitinase-like proteins, chitotriosidase (CHIT1), YKL-40, and acid mammalian chitinase, and summarizes the genetic studies of asthma and immune-mediated diseases with polymorphisms in the genes encoding these proteins, CHIT1, CHI3L1, and CHIA, respectively. RECENT FINDINGS Polymorphisms in the CHIT1, CHIA, and CHI3L1 genes influence chitotriosidase enzyme activity, acid mammalian chitinase activity, and YKL-40 levels, respectively. Regulatory SNPs in CHI3L1 were also associated with asthma, atopy, and immune-mediated diseases, and nonsynonymous SNPs in CHIA were associated with asthma. No CHIT1 polymorphisms, including a common nonfunctional 24-bp duplication allele, have been associated with asthma. SUMMARY These genes represent novel asthma susceptibility genes. Variations in CHI3L1 and CHIA have been associated with asthma risk. Polymorphisms in CHIT1 have not yet been associated with asthma, but few studies have been reported. Given that chitotriosidase is the major chitinase in the airways and a common nonfunctional allele is present in many populations, additional studies of this gene are warranted. Lastly, studies of all three genes need to be conducted in populations of diverse ancestries.","author":[{"dropping-particle":"","family":"Ober","given":"Carole","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chupp","given":"Geoffrey L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Current opinion in allergy and clinical immunology","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2009","10"]]},"page":"401-8","title":"The chitinase and chitinase-like proteins: a review of genetic and functional studies in asthma and immune-mediated diseases.","type":"article-journal","volume":"9"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1093/molbev/msw198","ISSN":"1537-1719","PMID":"27702777","abstract":"Acidic mammalian chitinase (AMCase) is implicated in asthma, allergic inflammation, and food processing. Little is known about genetic and evolutional regulation of chitinolytic activity of AMCase. Here, we relate human AMCase polymorphisms to the mouse AMCase, and show that the highly active variants encoded by nonsynonymous single-nucleotide polymorphisms (nsSNPs) are consistent with the mouse AMCase sequence. The chitinolytic activity of the recombinant human AMCase was significantly lower than that of the mouse counterpart. By creating mouse-human chimeric AMCase protein we found that the presence of the N-terminal region of human AMCase containing conserved active site residues reduced the enzymatic activity of the molecule. We were able to significantly increase the activity of human AMCase by amino acid substitutions encoded by nsSNPs (N45, D47, and R61) with those conserved in the mouse homologue (D45, N47, and M61). For abolition of the mouse AMCase activity, introduction of M61R mutation was sufficient. M61 is conserved in most of primates other than human and orangutan as well as in other mammals. Orangutan has I61 substitution, which also markedly reduced the activity of the mouse AMCase, indicating that the M61 is a crucial residue for the chitinolytic activity. Altogether, our data suggest that human AMCase has lost its chitinolytic activity by integration of nsSNPs during evolution and that the enzyme can be reactivated by introducing amino acids conserved in the mouse counterpart.","author":[{"dropping-particle":"","family":"Okawa","given":"Kazuaki","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ohno","given":"Misa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kashimura","given":"Akinori","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kimura","given":"Masahiro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kobayashi","given":"Yuki","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sakaguchi","given":"Masayoshi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sugahara","given":"Yasusato","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kamaya","given":"Minori","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kino","given":"Yoshihiro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bauer","given":"Peter O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oyama","given":"Fumitaka","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Molecular biology and evolution","id":"ITEM-2","issue":"12","issued":{"date-parts":[["2016"]]},"page":"3183-3193","title":"Loss and Gain of Human Acidic Mammalian Chitinase Activity by Nonsynonymous SNPs.","type":"article-journal","volume":"33"},"uris":[""]}],"mendeley":{"formattedCitation":"^80,82","plainTextFormattedCitation":"80,82","previouslyFormattedCitation":"^79,81"},"properties":{"noteIndex":0},"schema":""}80,82, and IgEADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.jaci.2008.04.030","ISSN":"1097-6825","PMID":"18602573","abstract":"BACKGROUND In view of the hygiene hypothesis and the involvement of acidic mammalian chitinase (CHIA) in the effector responses of IL-13 with asthma, CHIA (GeneID-27159) is a potential asthma candidate gene. OBJECTIVE To investigate the association of CHIA polymorphisms with atopic asthma and serum total IgE levels. METHODS Twenty-one single nucleotide polymorphisms were identified by sequencing DNA of 60 individuals. On the basis of linkage disequilibrium, 6 polymorphisms were selected and genotyped in unrelated atopic patients with asthma (N = 270) and controls (N = 292) and an independent pediatric cohort (patients, 150; controls, 101). Electrophoretic mobility shift assay and reporter gene assays were also performed. RESULTS The rs3806448G/A promoter polymorphism showed significant association with atopic asthma (P(adult) = .00001 and P(pediatric) = .0002) and serum total IgE (P < .05). Also rs2282290G/A was associated with atopic asthma (P(adult) = .00009 and P(pediatric) = .00003), whereas the rs10494132C/T polymorphism was associated with serum total IgE in the patients (P < .05). We also showed that the promoter single nucleotide polymorphisms altered the transcriptional activity of CHIA promoter and the C to T substitution at rs10494132 abrogated the Octamer transcription factor-1 (Oct-1) binding site. 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Interleukin 17 receptor D (IL17RD) at 3p14.3 encodes a membrane protein belonging to the interleukin-17 receptor (IL-17R) protein familyADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/nar/gkv1189","ISSN":"1362-4962","PMID":"26553804","abstract":"The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. 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The gene product affects fibroblast growth factor signaling, inhibiting or stimulating growth through MAPK/ERK signalingADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/nar/gkv1189","ISSN":"1362-4962","PMID":"26553804","abstract":"The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. 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Integrin subunit beta 8 (ITGB8) at 7p21.1 is a member of the integrin beta chain family and encodes a single-pass type I membrane protein that binds to an alpha subunit to form an integrin complexADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/nar/gkv1189","ISSN":"1362-4962","PMID":"26553804","abstract":"The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. 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The complexes mediate cell-cell and cell-extracellular matrix interactions and this complex plays a role in human airway epithelial proliferationADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/nar/gkv1189","ISSN":"1362-4962","PMID":"26553804","abstract":"The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. 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Little is known about the expression of integrins during human airway epithelial regeneration and differentiation after injury. We therefore characterized integrin subunit expression after mechanical injury in an in vivo xenograft model of human bronchial epithelium. On the migrating cells at the edges of surface epithelial wounds, there was increased expression of the alpha v-, beta 5-, beta 6-, and alpha 5-integrin subunits. During the later phase of repair, the increased expression of alpha v-, beta 5-, and beta 6-subunits persisted, but the expression of the beta 8-subunits was restricted to basal cells. In addition, there was a redistribution of the alpha 2- and alpha 6-collagen/laminin-binding integrins to suprabasal epithelial layers. There was no expression of the beta 3- or alpha 4-integrin subunit on reparative epithelium. A similar upregulation of alpha v-, beta 5-, and beta 6-integrin receptor subunits was observed in areas of undifferentiated airway from cystic fibrosis patients. Injured epithelium was found to be significantly more susceptible to gene transfer with a recombinant adenovirus, suggesting that the increased integrin expression has implications for the acquisition of adenovirus infections and for lung-directed gene therapy.","author":[{"dropping-particle":"","family":"Pilewski","given":"J M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Latoche","given":"J D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Arcasoy","given":"S M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Albelda","given":"S M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The American journal of physiology","id":"ITEM-1","issue":"1 Pt 1","issued":{"date-parts":[["1997","7"]]},"page":"L256-63","title":"Expression of integrin cell adhesion receptors during human airway epithelial repair in vivo.","type":"article-journal","volume":"273"},"uris":[""]}],"mendeley":{"formattedCitation":"⁸⁵","plainTextFormattedCitation":"85","previouslyFormattedCitation":"⁸⁴"},"properties":{"noteIndex":0},"schema":""}85. Expression of this protein is increased in COPDADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1074/jbc.M111.276790","ISSN":"1083-351X","PMID":"21878622","abstract":"The integrin αvβ8 is a cell surface receptor for the latent domain (LAP) of the multifunctional cytokine TGF-β. Through its association with LAP, TGF-β is maintained in a latent form that must be activated to function. Binding to the integrin αvβ8 with subsequent metalloproteolytic cleavage of LAP represents a major mechanism of TGF-β activation in vivo. Altered expression of the integrin β8 subunit (ITGB8) is found in human chronic obstructive pulmonary disease, cancers, and brain vascular malformations. We have previously shown that the proinflammatory cytokine interleukin-1β (IL-1β) increases ITGB8 expression on lung fibroblasts, which increases αvβ8-mediated TGF-β activation in fibrosis and pathologic inflammation. Here we report the mechanism of increased ITGB8 expression by IL-1β. Our data support a model where the chromatin architecture of the ITGB8 core promoter is altered by nucleosomal repositioning that enhances the interaction of an AP1 complex (containing c-Jun and ATF2). This repositioning is caused by the dissociation of HDAC2 with the ITGB8 core promoter, leading to increased histone H4 acetylation and a loosening of nucleosomal-DNA interactions allowing \"opening\" of the chromatin structure and increased association of c-Jun and ATF-2. These changes are mediated through NFκB- and p38-dependent pathways. Ultimately, these events culminate in increasing ITGB8 transcription, αvβ8 surface expression, and αvβ8-mediated TGFβ activation.","author":[{"dropping-particle":"","family":"Markovics","given":"Jennifer A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Araya","given":"Jun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cambier","given":"Stephanie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Somanath","given":"Sangeeta","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gline","given":"Stephanie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jablons","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hill","given":"Arthur","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wolters","given":"Paul J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nishimura","given":"Stephen L","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Journal of biological chemistry","id":"ITEM-1","issue":"42","issued":{"date-parts":[["2011","10","21"]]},"page":"36864-74","title":"Interleukin-1beta induces increased transcriptional activation of the transforming growth factor-beta-activating integrin subunit beta8 through altering chromatin architecture.","type":"article-journal","volume":"286"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1172/JCI45589","ISSN":"1558-8238","PMID":"21646718","abstract":"The airway is a primary portal of entry for noxious environmental stimuli that can trigger airway remodeling, which contributes significantly to airway obstruction in chronic obstructive pulmonary disease (COPD) and chronic asthma. Important pathologic components of airway remodeling include fibrosis and abnormal innate and adaptive immune responses. The positioning of fibroblasts in interstitial spaces suggests that they could participate in both fibrosis and chemokine regulation of the trafficking of immune cells such as dendritic cells, which are crucial antigen-presenting cells. However, physiological evidence for this dual role for fibroblasts is lacking. Here, in two physiologically relevant models - conditional deletion in mouse fibroblasts of the TGF-β-activating integrin αvβ8 and neutralization of αvβ8 in human COPD fibroblasts - we have elucidated a mechanism whereby lung fibroblast chemokine secretion directs dendritic cell trafficking, in a manner that is critically dependent on αvβ8-mediated activation of TGF-β by fibroblasts. 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A major mechanism of airway obstruction in COPD is thickening of the small airway walls. We asked whether SM actively contributes to airway wall thickening through alteration of epithelial-mesenchymal interactions in COPD. Using immunohistochemical staining, airway morphometry, and fibroblast culture of lung samples from COPD patients; genome-wide analysis of an in vitro model of SM; and in vitro modeling of human airway epithelial-mesenchymal interactions, we provide evidence that SM, through the increased secretion of IL-1beta, induces a fibrotic response in adjacent airway fibroblasts. We identify a pivotal role for integrin-mediated TGF-beta activation in amplifying SM and driving IL-1beta-dependent profibrotic mesenchymal responses. Finally, we show that SM correlates with increased severity of COPD and that fibroblast expression of the integrin alpha(v)beta(8), which is the major mediator of airway fibroblast TGF-beta activation, correlated with disease severity and small airway wall thickening in COPD. 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Within the well-phenotyped COPDGene cohort, we identified variable effects of these variants on emphysema and airway phenotypes which recapitulated clinically and epidemiologically-observed emphysema-predominant presentation of COPDADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"0903-1936","PMID":"12662000","abstract":"Patients with fixed airflow limitation are grouped under the heading of chronic obstructive pulmonary disease (COPD). The authors investigated whether COPD patients have distinct functional, radiological and sputum cells characteristics depending on the presence or absence of emphysema. Twenty-four COPD outpatients, 12 with and 12 without emphysema on high-resolution computed tomography scan of the chest, were examined. Patients underwent chest radiography, pulmonary function tests and sputum induction and analysis. Subjects with documented emphysema had lower forced expiratory volume in one second (FEV1), FEV1/forced vital capacity ratio, and lower carbon monoxide diffusion constant (K(CO)), compared with subjects without emphysema. Chest radiograph score of emphysema was higher, chest radiograph score of chronic bronchitis was lower, and the number of sputum lymphocytes was increased in patients with emphysema, who also showed a negative correlation between K(CO) and pack-yrs. Chronic obstructive pulmonary disease patients with emphysema, documented by high-resolution computed tomography scan, have a different disease phenotype compared with patients without emphysema. Identification of chronic obstructive pulmonary disease-related phenotypes may improve understanding of the natural history and treatment of the disease.","author":[{"dropping-particle":"","family":"Boschetto","given":"P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miniati","given":"M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Miotto","given":"D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Braccioni","given":"F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rosa","given":"E","non-dropping-particle":"De","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bononi","given":"I","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Papi","given":"A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saetta","given":"M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fabbri","given":"L M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mapp","given":"C E","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The European respiratory journal","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2003","3"]]},"page":"450-4","title":"Predominant emphysema phenotype in chronic obstructive pulmonary.","type":"article-journal","volume":"21"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1136/thoraxjnl-2013-203601","ISSN":"1468-3296","PMID":"24563194","abstract":"BACKGROUND There is notable heterogeneity in the clinical presentation of patients with COPD. To characterise this heterogeneity, we sought to identify subgroups of smokers by applying cluster analysis to data from the COPDGene study. METHODS We applied a clustering method, k-means, to data from 10 192 smokers in the COPDGene study. After splitting the sample into a training and validation set, we evaluated three sets of input features across a range of k (user-specified number of clusters). Stable solutions were tested for association with four COPD-related measures and five genetic variants previously associated with COPD at genome-wide significance. The results were confirmed in the validation set. FINDINGS We identified four clusters that can be characterised as (1) relatively resistant smokers (ie, no/mild obstruction and minimal emphysema despite heavy smoking), (2) mild upper zone emphysema-predominant, (3) airway disease-predominant and (4) severe emphysema. All clusters are strongly associated with COPD-related clinical characteristics, including exacerbations and dyspnoea (p<0.001). We found strong genetic associations between the mild upper zone emphysema group and rs1980057 near HHIP, and between the severe emphysema group and rs8034191 in the chromosome 15q region (p<0.001). All significant associations were replicated at p<0.05 in the validation sample (12/12 associations with clinical measures and 2/2 genetic associations). INTERPRETATION Cluster analysis identifies four subgroups of smokers that show robust associations with clinical characteristics of COPD and known COPD-associated genetic variants.","author":[{"dropping-particle":"","family":"Castaldi","given":"Peter J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dy","given":"Jennifer","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ross","given":"James","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chang","given":"Yale","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Washko","given":"George R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Curran-Everett","given":"Douglas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Williams","given":"Andre","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lynch","given":"David A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Make","given":"Barry J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crapo","given":"James D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bowler","given":"Russ P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Regan","given":"Elizabeth A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hokanson","given":"John E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kinney","given":"Greg L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Han","given":"Meilan K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Soler","given":"Xavier","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ramsdell","given":"Joseph W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barr","given":"R Graham","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Foreman","given":"Marilyn","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beek","given":"Edwin","non-dropping-particle":"van","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Casaburi","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Criner","given":"Gerald J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lutz","given":"Sharon M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rennard","given":"Steven I","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Santorico","given":"Stephanie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sciurba","given":"Frank C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"DeMeo","given":"Dawn L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hersh","given":"Craig P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"Edwin K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Thorax","id":"ITEM-2","issue":"5","issued":{"date-parts":[["2014","5"]]},"page":"415-22","title":"Cluster analysis in the COPDGene study identifies subtypes of smokers with distinct patterns of airway disease and emphysema.","type":"article-journal","volume":"69"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.3109/15412555.2012.727920","ISSN":"1541-2563","PMID":"23272662","abstract":"BACKGROUND Early identification of patients with COPD and prone to more rapid decline in lung function is of particular interest from both a prognostic and therapeutic point of view. The aim of this study was to identify the clinical, functional and imaging characteristics associated with the rapid FEV(1) decline in COPD. METHODS Between 2001 and 2005, 131 outpatients with moderate COPD in stable condition under maximum inhaled therapy underwent clinical interview, pulmonary function tests and HRCT imaging of the chest and were followed for at least 3 years. RESULTS Twenty-six percent of patients had emphysema detected visually using HRCT. The FEV(1) decline was 42 ± 66 mL/y in the total sample, 88 ± 76 mL/y among rapid decliners and 6 ± 54 mL/y among the other patients. In the univariable analysis, the decline of FEV(1) was positively associated with pack-years (p < 0.05), emphysema at HRCT (p < 0.001), RV (p < 0.05), FRC (p < 0.05), FEV(1) (p < 0.01) at baseline and with number of hospitalizations per year (p < 0.05) during the follow-up. Using multivariable analysis, the presence of emphysema proved to be an independent prognostic factor of rapid decline (p = 0.001). When emphysema was replaced by RV, the model still remained significant. CONCLUSIONS The rapid decline in lung function may be identified by the presence of emphysema at HRCT or increased RV in patients with a long smoking history.","author":[{"dropping-particle":"","family":"Cerveri","given":"Isa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Corsico","given":"Angelo G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grosso","given":"Amelia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Albicini","given":"Federica","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ronzoni","given":"Vanessa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tripon","given":"Bianca","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Imberti","given":"Federica","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Galasso","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klersy","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Luisetti","given":"Maurizio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pistolesi","given":"Massimo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"COPD","id":"ITEM-3","issue":"1","issued":{"date-parts":[["2013","2"]]},"page":"55-61","title":"The rapid FEV(1) decline in chronic obstructive pulmonary disease is associated with predominant emphysema: a longitudinal study.","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"^89–91","plainTextFormattedCitation":"89–91","previouslyFormattedCitation":"^88–90"},"properties":{"noteIndex":0},"schema":""}89–91. We also identified other features including computed tomography (CT) subtypes, intensity and status of smoking, diffusing capacity of carbon monoxide, asthma, and inflammatory biomarkers, which are supported by previous findings [ref]. The identification of variable COPD risk loci associations with sub-phenotypes indicates possible implications for more nuanced approaches to therapy for COPD.Analyzing over hundreds of traits in GWAS Catalog, we identified overlapping associations with various diseases/traits in multiple organ systems. Some overlapping diseases were recognized as comorbidities including coronary artery disease, bone mineral density, and type 2 diabetes mellitus. variable associations with multiple traits among COPD-associated loci again highlight the more heterogeneous and diverse biological mechanism as seen in other diseasesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1471-2466-14-164","ISSN":"1471-2466","PMID":"25341556","abstract":"BACKGROUND Chronic obstructive pulmonary disease (COPD) has been classically divided into blue bloaters and pink puffers. The utility of these clinical subtypes is unclear. However, the broader distinction between airway-predominant and emphysema-predominant COPD may be clinically relevant. The objective was to define clinical features of emphysema-predominant and non-emphysematous COPD patients. METHODS Current and former smokers from the Genetic Epidemiology of COPD Study (COPDGene) had chest computed tomography (CT) scans with quantitative image analysis. Emphysema-predominant COPD was defined by low attenuation area at -950 Hounsfield Units (LAA-950) ≥10%. Non-emphysematous COPD was defined by airflow obstruction with minimal to no emphysema (LAA-950 < 5%). RESULTS Out of 4197 COPD subjects, 1687 were classified as emphysema-predominant and 1817 as non-emphysematous; 693 had LAA-950 between 5-10% and were not categorized. Subjects with emphysema-predominant COPD were older (65.6 vs 60.6 years, p < 0.0001) with more severe COPD based on airflow obstruction (FEV1 44.5 vs 68.4%, p < 0.0001), greater exercise limitation (6-minute walk distance 1138 vs 1331 ft, p < 0.0001) and reduced quality of life (St. George's Respiratory Questionnaire score 43 vs 31, p < 0.0001). Self-reported diabetes was more frequent in non-emphysematous COPD (OR 2.13, p < 0.001), which was also confirmed using a strict definition of diabetes based on medication use. The association between diabetes and non-emphysematous COPD was replicated in the ECLIPSE study. CONCLUSIONS Non-emphysematous COPD, defined by airflow obstruction with a paucity of emphysema on chest CT scan, is associated with an increased risk of diabetes. COPD patients without emphysema may warrant closer monitoring for diabetes, hypertension, and hyperlipidemia and vice versa. TRIAL REGISTRATION identifiers: COPDGene NCT00608764, ECLIPSE NCT00292552.","author":[{"dropping-particle":"","family":"Hersh","given":"Craig P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Make","given":"Barry J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lynch","given":"David A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barr","given":"R Graham","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bowler","given":"Russell P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Calverley","given":"Peter M A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Castaldi","given":"Peter J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cho","given":"Michael H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Coxson","given":"Harvey O","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"DeMeo","given":"Dawn L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Foreman","given":"Marilyn G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Han","given":"MeiLan K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harshfield","given":"Benjamin J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hokanson","given":"John E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lutz","given":"Sharon","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ramsdell","given":"Joe W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Regan","given":"Elizabeth A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rennard","given":"Stephen I","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schroeder","given":"Joyce D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sciurba","given":"Frank C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Steiner","given":"Robert M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tal-Singer","given":"Ruth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beek","given":"Edwin","non-dropping-particle":"van","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Silverman","given":"Edwin K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crapo","given":"James D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"COPDGene and ECLIPSE Investigators","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"BMC pulmonary medicine","id":"ITEM-1","issued":{"date-parts":[["2014","10","24"]]},"page":"164","title":"Non-emphysematous chronic obstructive pulmonary disease is associated with diabetes mellitus.","type":"article-journal","volume":"14"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1371/journal.pone.0148794","ISSN":"1932-6203","PMID":"26866482","abstract":"BACKGROUND Epicardial adipose tissue (EAT) has been shown to be a non-invasive marker that predicts the progression of cardiovascular disease (CVD). It has been reported that the EAT volume is increased in patients with chronic obstructive pulmonary disease (COPD). However, little is known about which phenotypes of COPD are associated with increased EAT. METHODS One hundred and eighty smokers who were referred to the clinic were consecutively enrolled. A chest CT was used for the quantification of the emphysematous lesions, airway lesions, and EAT. These lesions were assessed as the percentage of low attenuation volume (LAV%), the square root of airway wall area of a hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10) and the EAT area, respectively. The same measurements were made on 225 Vietnamese COPD patients to replicate the results. RESULTS Twenty-six of the referred patients did not have COPD, while 105 were diagnosed as having COPD based on a FEV1/FVC<0.70. The EAT area was significantly associated with age, BMI, FEV1 (%predicted), FEV1/FVC, self-reported hypertension, self-reported CVD, statin use, LAV%, and √Aaw at Pi10 in COPD patients. The multiple regression analyses showed that only BMI, self-reported CVD and √Aaw at Pi10 were independently associated with the EAT area (R2 = 0.51, p<0.0001). These results were replicated in the Vietnamese population. CONCLUSIONS The EAT area is independently associated with airway wall thickness. Because EAT is also an independent predictor of CVD risk, these data suggest a mechanistic link between the airway predominant form of COPD and CVD.","author":[{"dropping-particle":"","family":"Higami","given":"Yuichi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ogawa","given":"Emiko","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ryujin","given":"Yasushi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Goto","given":"Kenichi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seto","given":"Ruriko","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wada","given":"Hiroshi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"Van","family":"Tho","given":"Nguyen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lan","given":"Le Thi Tuyet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Paré","given":"Peter D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nakano","given":"Yasutaka","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PloS one","id":"ITEM-2","issue":"2","issued":{"date-parts":[["2016"]]},"page":"e0148794","title":"Increased Epicardial Adipose Tissue Is Associated with the Airway Dominant Phenotype of Chronic Obstructive Pulmonary Disease.","type":"article-journal","volume":"11"},"uris":[""]}],"mendeley":{"formattedCitation":"^92,93","plainTextFormattedCitation":"92,93","previouslyFormattedCitation":"^91,92"},"properties":{"noteIndex":0},"schema":""}92,93.This study also enhances or understanding about relationship between COPD with asthma and pulmonary fibrosis. Extending from previously reported genetic correlationsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3752","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","ISSN":"1546-1718","PMID":"28166215","abstract":"Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. We performed a genetic association study in 15,256 cases and 47,936 controls, with replication of select top results (P < 5 x 10-6) in 9,498 cases and 9,748 controls. In the combined meta-analysis, we identified 22 loci associated at genome-wide significance, including 13 new associations with COPD. Nine of these 13 loci have been associated with lung function in general population samples, while 4 (EEFSEC, DSP, MTCL1, and SFTPD) are new. We noted two loci shared with pulmonary fibrosis (FAM13A and DSP) but that had opposite risk alleles for COPD. None of our loci overlapped with genome-wide associations for asthma, although one locus has been implicated in joint susceptibility to asthma and obesity. We also identified genetic correlation between COPD and asthma. Our findings highlight new loci associated with COPD, demonstrate the importance of specific loci associated with lung function to COPD, and identify potential regions of genetic overlap between COPD and other respiratory diseases.","author":[{"dropping-particle":"","family":"Hobbs","given":"Brian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jong","given":"Kim","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lamontagne","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bosse","given":"Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shrine","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Artigas","given":"María 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Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Paré, Peter D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; COPDGene Investigators; ECLIPSE Investigators; LifeLines Investigators; SPIROMICS Research Group; International COPD Genetics Network Investigators; UK BiLEVE Investigators; International COPD Genetics Consortium; Bosse, Y; Shrine, Nick; Artigas, María Soler; Wain, Louise V; Hall, Ian P; Jackson, Victoria E; Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Pare, P D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Consortium Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium Network Investigators)\n\nFrom Duplicate 1 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.\n\nFrom Duplicate 2 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.","page":"426-432","title":"Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis","type":"article-journal","volume":"49"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵","plainTextFormattedCitation":"5","previouslyFormattedCitation":"⁵"},"properties":{"noteIndex":0},"schema":""}5, we identified 19 genetic loci shared between these pairs of diseases. In addition to two previously reported overlapping loci for COPD and IPF (near FAM13A and DSP), we identified two loci near ZKSCAN1 (7q22.1) and CRHR1 (17q21.31). We identified multiple overlapping loci with asthma including 2p24.3 (DDX1), 6p21.33, 6p22.1, 9p21.3 (ELAVL2), 19q13.32 (RSPH6A). The locus near DDX was associated in the combined analysis of COPD and asthmaADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1183/09031936.00001914","ISSN":"1399-3003","PMID":"24993907","abstract":"Asthma and chronic obstructive pulmonary disease (COPD) are thought to share a genetic background (\"Dutch hypothesis\"). We investigated whether asthma and COPD have common underlying genetic factors, performing genome-wide association studies for both asthma and COPD and combining the results in meta-analyses. Three loci showed potential involvement in both diseases: chr2p24.3, chr5q23.1 and chr13q14.2, containing DDX1, COMMD10 (both participating in the nuclear factor (NF) κβ pathway) and GNG5P5, respectively. Single nucleotide polymorphisms (SNPs) rs9534578 in GNG5P5 reached genome-wide significance after first replication phase (p=9.96×10(-9)). The second replication phase, in seven independent cohorts, provided no significant replication. Expression quantitative trait loci (eQTL) analysis in blood cells and lung tissue on the top 20 associated SNPs identified two SNPs in COMMD10 that influenced gene expression. Inflammatory processes differ in asthma and COPD and are mediated by NF-κβ, which could be driven by the same underlying genes, COMMD10 and DDX1. None of the SNPs reached genome-wide significance. Our eQTL studies support a functional role for two COMMD10 SNPs, since they influence gene expression in both blood cells and lung tissue. Our findings suggest that there is either no common genetic component in asthma and COPD or, alternatively, different environmental factors, e.g. lifestyle and occupation in different countries and continents, which may have obscured the genetic common contribution.","author":[{"dropping-particle":"","family":"Smolonska","given":"Joanna","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Koppelman","given":"Gerard H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wijmenga","given":"Cisca","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vonk","given":"Judith M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zanen","given":"Pieter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bruinenberg","given":"Marcel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Curjuric","given":"Ivan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Imboden","given":"Medea","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Thun","given":"Gian-Andri","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Franke","given":"Lude","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Probst-Hensch","given":"Nicole M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nürnberg","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Riemersma","given":"Roland A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schayck","given":"Constant P","non-dropping-particle":"van","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loth","given":"Daan W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brusselle","given":"Guy G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Stricker","given":"Bruno H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hofman","given":"Albert","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Uitterlinden","given":"André G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lahousse","given":"Lies","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"London","given":"Stephanie J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Loehr","given":"Laura R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Manichaikul","given":"Ani","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barr","given":"R Graham","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Donohue","given":"Kathleen M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rich","given":"Stephen S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pare","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bossé","given":"Yohan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hao","given":"Ke","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berge","given":"Maarten","non-dropping-particle":"van den","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Groen","given":"Harry J M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lammers","given":"Jan-Willem J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mali","given":"Willem","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Boezen","given":"H Marike","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Postma","given":"Dirkje S","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The European respiratory journal","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2014","10"]]},"page":"860-72","title":"Common genes underlying asthma and COPD? Genome-wide analysis on the Dutch hypothesis.","type":"article-journal","volume":"44"},"uris":[""]}],"mendeley":{"formattedCitation":"⁹⁴","plainTextFormattedCitation":"94","previouslyFormattedCitation":"⁹³"},"properties":{"noteIndex":0},"schema":""}94, and is involved in NFkB pathwayADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/jcb.22004","ISSN":"1097-4644","PMID":"19058135","abstract":"DEAD-box RNA helicases constitute the largest family of RNA helicases and are involved in many aspects of RNA metabolism. In this study, we identified RelA (p65), a subunit of nuclear factor-kappaB (NF-kappaB), as a cellular co-factor of DEAD-box RNA helicase DDX1, through mammalian two hybrid system and co-immunoprecipitation assay. Additionally, confocal microscopy and chromatin immunoprecipitation assays confirmed this interaction. In NF-kappaB dependent reporter gene assay, DDX1 acted as a co-activator to enhance NF-kappaB-mediated transcription activation. The functional domains involved were mapped to the carboxy terminal transactivation domain of RelA and the amino terminal ATPase/helicase domain of DDX1. The DDX1 trans-dominant negative mutant lacking ATP-dependent RNA helicase activity lost it transcriptional inducer activity. Moreover, depletion of endogenous DDX1 by specific small interfering RNAs significantly reduced NF-kappaB-dependent transcription. Taken together, the results suggest that DDX1 may play an important role in NF-kappaB-mediated transactivation, and revelation of this regulatory pathway may help to explore the novel mechanisms for regulating NF-kappaB transcriptional activity.","author":[{"dropping-particle":"","family":"Ishaq","given":"Musarat","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ma","given":"Li","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wu","given":"Xiaoyun","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mu","given":"Yongxin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pan","given":"Ji'an","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Jiajie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hu","given":"Tao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fu","given":"Qiong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Guo","given":"Deyin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of cellular biochemistry","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2009","2","1"]]},"page":"296-305","title":"The DEAD-box RNA helicase DDX1 interacts with RelA and enhances nuclear factor kappaB-mediated transcription.","type":"article-journal","volume":"106"},"uris":[""]}],"mendeley":{"formattedCitation":"⁹⁵","plainTextFormattedCitation":"95","previouslyFormattedCitation":"⁹⁴"},"properties":{"noteIndex":0},"schema":""}95, leading to a possible shared inflammatory mechanism between these diseases. We also identified genetic overlap between COPD and lung cancer, confirming previous findingsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"0140-6736","PMID":"95731","abstract":"First-degree relatives of lung-cancer patients and of patients with chronic obstructive pulmonary disease had significantly higher age-sex-race-smoking-adjusted rates of impaired forced expiration than first-degree relatives of patients with non-pulmonary disease or community-derived comparison series (neighbourhood controls and teachers). Subclassification of the data and multiple adjustment for smoking, race, sex, and other confounding factors emphasised the consistency of the pattern. These findings strongly suggest that lung cancer and chronic obstructive pulmonary disease share a common familial component other than smoking. The clinical manifestation may depend on the presence of one or more other cofactors as yet undefined.","author":[{"dropping-particle":"","family":"Cohen","given":"B H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Diamond","given":"E L","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Graves","given":"C G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kreiss","given":"P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Levy","given":"D A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Menkes","given":"H A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Permutt","given":"S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Quaskey","given":"S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tockman","given":"M S","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Lancet (London, England)","id":"ITEM-1","issue":"8037","issued":{"date-parts":[["1977","9","10"]]},"page":"523-6","title":"A common familial component in lung cancer and chronic obstructive pulmonary disease.","type":"article-journal","volume":"2"},"uris":[""]}],"mendeley":{"formattedCitation":"⁹⁶","plainTextFormattedCitation":"96","previouslyFormattedCitation":"⁹⁵"},"properties":{"noteIndex":0},"schema":""}96. In this study, we identified three overlapping loci including 6p21, 15q25, and 19q13, which were previously reportedADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pone.0016476","ISSN":"1932-6203","PMID":"21304900","abstract":"Epidemiological studies show that approximately 20-30% of chronic smokers develop chronic obstructive pulmonary disease (COPD) while 10-15% develop lung cancer. COPD pre-exists lung cancer in 50-90% of cases and has a heritability of 40-77%, much greater than for lung cancer with heritability of 15-25%. These data suggest that smokers susceptible to COPD may also be susceptible to lung cancer. This study examines the association of several overlapping chromosomal loci, recently implicated by GWA studies in COPD, lung function and lung cancer, in (n = 1400) subjects sub-phenotyped for the presence of COPD and matched for smoking exposure. Using this approach we show; the 15q25 locus confers susceptibility to lung cancer and COPD, the 4q31 and 4q22 loci both confer a reduced risk to both COPD and lung cancer, the 6p21 locus confers susceptibility to lung cancer in smokers with pre-existing COPD, the 5p15 and 1q23 loci both confer susceptibility to lung cancer in those with no pre-existing COPD. We also show the 5q33 locus, previously associated with reduced FEV(1), appears to confer susceptibility to both COPD and lung cancer. The 6p21 locus previously linked to reduced FEV(1) is associated with COPD only. Larger studies will be needed to distinguish whether these COPD-related effects may reflect, in part, associations specific to different lung cancer histology. We demonstrate that when the \"risk genotypes\" derived from the univariate analysis are incorporated into an algorithm with clinical variables, independently associated with lung cancer in multivariate analysis, modest discrimination is possible on receiver operator curve analysis (AUC = 0.70). We suggest that genetic susceptibility to lung cancer includes genes conferring susceptibility to COPD and that sub-phenotyping with spirometry is critical to identifying genes underlying the development of lung cancer.","author":[{"dropping-particle":"","family":"Young","given":"Robert P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hopkins","given":"Raewyn J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Whittington","given":"Chris F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"Bryan A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Epton","given":"Michael J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gamble","given":"Gregory D","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PloS one","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2011","2","3"]]},"page":"e16476","title":"Individual and cumulative effects of GWAS susceptibility loci in lung cancer: associations after sub-phenotyping for COPD.","type":"article-journal","volume":"6"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1038/ejhg.2014.229","ISSN":"1476-5438","PMID":"25407004","abstract":"Recent genome-wide association studies implicated that the nicotinic acetylcholine receptors (nAChRs) are common susceptible genes of two contextual diseases: chronic obstructive pulmonary disease (COPD) and lung cancer. We aimed to test whether the copy number variations (CNVs) in nAChRs have hereditary contributions to development of the two diseases. In two, two-stage, case-control studies of southern and eastern Chinese, a common CNV-3956 that duplicates the cholinergic receptor, nicotinic, α7 (CHRNA7) gene was genotyped in a total of 7880 subjects and its biological phenotype was assessed. The ≥4-copy of CNV-3956 increased COPD risk (≥4-copy vs 2/3-copy: OR=1.44, 95% CI=1.23-1.68) and caused poor lung function, and it similarly augmented risk (OR=1.49, 95% CI=1.29-1.73) and worsened prognosis (hazard ratio (HR)=1.25, 95% CI=1.07-1.45) of lung cancer. The ≥4-copy was estimated to account for 1.56% of COPD heritability and 1.87% of lung cancer heritability, respectively. Phenotypic analysis further showed that the ≥4-copy of CNV-3956 improved CHRNA7 expression in vivo and increased the carriers' smoking amount. The CNV-3956 of CHRNA7 contributed to increased risks and poor prognoses of both COPD and lung cancer, and this may be a genetic biomarker of the two diseases.","author":[{"dropping-particle":"","family":"Yang","given":"Lei","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Xiaoxiao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Qiu","given":"Fuman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fang","given":"Wenxiang","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Lisha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huang","given":"Dongsheng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Chenli","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhong","given":"Nanshan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ran","given":"Pixin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhou","given":"Yifeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lu","given":"Jiachun","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"European journal of human genetics : EJHG","id":"ITEM-2","issue":"8","issued":{"date-parts":[["2015","8"]]},"page":"1019-24","title":"Duplicated copy of CHRNA7 increases risk and worsens prognosis of COPD and lung cancer.","type":"article-journal","volume":"23"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1016/j.pupt.2013.05.003","ISSN":"1522-9629","PMID":"23701918","abstract":"Chronic obstructive pulmonary disease (COPD) affects more than 200 million people worldwide and is expected to become the third leading cause of death in 2020. COPD is characterized by progressive airflow limitation, due to a combination of chronic inflammation and remodeling of the small airways (bronchiolitis) and loss of elastic recoil caused by destruction of the alveolar walls (emphysema). Lung cancer is the most important cause of cancer-related death in the world. (Cigarette) smoking is the principal culprit causing both COPD and lung cancer; in addition, exposure to environmental tobacco smoke, biomass fuel smoke, coal smoke and outdoor air pollution have also been associated with an increased incidence of both diseases. Importantly, smokers with COPD--defined as either not fully reversible airflow limitation or emphysema--have a two- to four-fold increased risk to develop lung cancer. In this review, we highlight several of the genetic, epigenetic and inflammatory mechanisms, which link COPD and carcinogenesis in the lungs. Elucidating the biological pathways and networks, which underlie the increased susceptibility of lung cancer in patients with COPD, has important implications for screening, prevention, diagnosis and treatment of these two devastating pulmonary diseases.","author":[{"dropping-particle":"","family":"Vermaelen","given":"K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brusselle","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Pulmonary pharmacology & therapeutics","id":"ITEM-3","issue":"5","issued":{"date-parts":[["2013","10"]]},"page":"544-54","title":"Exposing a deadly alliance: novel insights into the biological links between COPD and lung cancer.","type":"article-journal","volume":"26"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1016/j.semcancer.2016.11.001","ISSN":"1096-3650","PMID":"27840275","abstract":"Genetic variations in COPD and lung cancer may be one of the molecular mechanisms responsible for COPD-lung cancer transformation. The present review highlights main genetic variations co-existed in COPD and lung cancer and integrates the varied genes into four molecular mechanisms, e.g. activated cell proliferation pathway, tumor suppressor and DNA repair gene dysfunction, chronic inflammatory microenvironment, and impaired immune response, by which COPD epithelial cells may be transformed into tumorigenic status. We call special attention to further investigate the transformation from COPD to lung cancer and understand the exact molecular mechanisms, to prevent and reduce the increased incidence of COPD and lung cancer. We call direct evidence to show the existence of COPD-lung cancer transformation, since current evidence to support the transformation from COPD to lung cancer is the co-existence of selected genes and proteins in both. We should furthermore explore and understand the homogeneity and heterogeneity of gene mutation, epigenetics, sequencing, and function between COPD and lung cancer. The defining and understanding of COPD-lung cancer transformation will provide new diagnostic and therapeutic biomarkers as a new milestone to prevent and treat lung cancer.","author":[{"dropping-particle":"","family":"Wang","given":"Diane C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shi","given":"Lin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhu","given":"Zhenhua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gao","given":"Danyan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Yong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Seminars in cancer biology","id":"ITEM-4","issued":{"date-parts":[["2017"]]},"page":"52-59","title":"Genomic mechanisms of transformation from chronic obstructive pulmonary disease to lung cancer.","type":"article-journal","volume":"42"},"uris":[""]}],"mendeley":{"formattedCitation":"^97–100","plainTextFormattedCitation":"97–100","previouslyFormattedCitation":"^96–99"},"properties":{"noteIndex":0},"schema":""}97–100. The proportion of this shared risk due to smoking is not known; in epidemiologic studies, COPD confers greater risk to lung cancer independent of smokingADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1158/1940-6207.CAPR-12-0042","ISSN":"1940-6215","PMID":"22491518","abstract":"Chronic obstructive pulmonary disease (COPD) is defined as a disease causing an airflow limitation that is not fully reversible. COPD is phenotypically complex and characterized by small-airway disease and/or emphysema that result from the interaction between host genetic susceptibility and environmental exposures. As in lung cancer, smoking exposure is the most important risk factor for the development of COPD, accounting for 80% to 90% of all cases. COPD affects an estimated 8% to 10% of the general adult population, 15% to 20% of the smoking population, and 50% to 80% of lung cancer patients (with substantial smoking histories). In prospective studies, COPD has been found to be an independent risk factor for lung cancer, conferring a three- to 10-fold increased risk of lung cancer when compared with smokers without COPD. These findings suggest that smokers have a host susceptibility to COPD alone, COPD and lung cancer (i.e., overlap), and lung cancer in the absence of COPD. This minireview focuses on important points that need to be addressed when studying genetic susceptibility factors for COPD and its complex relationship with susceptibility to lung cancer.","author":[{"dropping-particle":"","family":"El-Zein","given":"Randa A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Young","given":"Robert P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hopkins","given":"Raewyn J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Etzel","given":"Carol J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Cancer prevention research (Philadelphia, Pa.)","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2012","4"]]},"page":"522-7","title":"Genetic predisposition to chronic obstructive pulmonary disease and/or lung cancer: important considerations when evaluating risk.","type":"article-journal","volume":"5"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.5306/wjco.v5.i4.660","ISSN":"2218-4333","PMID":"25300704","abstract":"The association between chronic obstructive pulmonary disease (COPD) and lung cancer has long been a subject of intense debate. The high prevalence of COPD in elderly smokers inevitably strengthens their coincidence. In addition to this contingent coincidence, recent studies have revealed a close association between the two diseases that is independent of the smoking history; that is, the existence of COPD is an independent risk factor for the development of lung cancer. Molecular-based evidence has been accumulating as a result of the efforts to explain the underlying mechanisms of this association. These mechanisms may include the following: the retention of airborne carcinogens followed by the activation of oncogenes and the suppression of tumor suppressor genes; the complex molecular mechanism associated with chronic inflammation in the distal airways of patients with COPD; the possible involvement of putative distal airway stem cells; and genetic factors that are common to both COPD and lung cancer. The existence of COPD in patients with lung cancer may potentially affect the process of diagnosis, surgical resection, radiotherapy, chemotherapy, and end-of-life care. The comprehensive management of COPD is extremely important for the appropriate treatment of lung cancer. Surgical resections with the aid of early interventions for COPD are often possible, even for patients with mild-to-moderate COPD. New challenges, such as lung cancer CT screening for individuals at high risk, are now in the process of being implemented. Evaluating the risk of lung cancer in patients with COPD may be warranted in community-based lung cancer screening.","author":[{"dropping-particle":"","family":"Takiguchi","given":"Yuichi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sekine","given":"Ikuo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Iwasawa","given":"Shunichiro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kurimoto","given":"Ryota","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatsumi","given":"Koichiro","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World journal of clinical oncology","id":"ITEM-2","issue":"4","issued":{"date-parts":[["2014","10","10"]]},"page":"660-6","title":"Chronic obstructive pulmonary disease as a risk factor for lung cancer.","type":"article-journal","volume":"5"},"uris":[""]}],"mendeley":{"formattedCitation":"^101,102","plainTextFormattedCitation":"101,102","previouslyFormattedCitation":"^100,101"},"properties":{"noteIndex":0},"schema":""}101,102, with many shared plausible pathwaysADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pone.0016476","ISSN":"1932-6203","PMID":"21304900","abstract":"Epidemiological studies show that approximately 20-30% of chronic smokers develop chronic obstructive pulmonary disease (COPD) while 10-15% develop lung cancer. COPD pre-exists lung cancer in 50-90% of cases and has a heritability of 40-77%, much greater than for lung cancer with heritability of 15-25%. These data suggest that smokers susceptible to COPD may also be susceptible to lung cancer. This study examines the association of several overlapping chromosomal loci, recently implicated by GWA studies in COPD, lung function and lung cancer, in (n = 1400) subjects sub-phenotyped for the presence of COPD and matched for smoking exposure. Using this approach we show; the 15q25 locus confers susceptibility to lung cancer and COPD, the 4q31 and 4q22 loci both confer a reduced risk to both COPD and lung cancer, the 6p21 locus confers susceptibility to lung cancer in smokers with pre-existing COPD, the 5p15 and 1q23 loci both confer susceptibility to lung cancer in those with no pre-existing COPD. We also show the 5q33 locus, previously associated with reduced FEV(1), appears to confer susceptibility to both COPD and lung cancer. The 6p21 locus previously linked to reduced FEV(1) is associated with COPD only. Larger studies will be needed to distinguish whether these COPD-related effects may reflect, in part, associations specific to different lung cancer histology. We demonstrate that when the \"risk genotypes\" derived from the univariate analysis are incorporated into an algorithm with clinical variables, independently associated with lung cancer in multivariate analysis, modest discrimination is possible on receiver operator curve analysis (AUC = 0.70). We suggest that genetic susceptibility to lung cancer includes genes conferring susceptibility to COPD and that sub-phenotyping with spirometry is critical to identifying genes underlying the development of lung cancer.","author":[{"dropping-particle":"","family":"Young","given":"Robert P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hopkins","given":"Raewyn J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Whittington","given":"Chris F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hay","given":"Bryan A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Epton","given":"Michael J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gamble","given":"Gregory D","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PloS one","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2011","2","3"]]},"page":"e16476","title":"Individual and cumulative effects of GWAS susceptibility loci in lung cancer: associations after sub-phenotyping for COPD.","type":"article-journal","volume":"6"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1097/MCP.0b013e32832c975a","ISSN":"1531-6971","PMID":"19417670","abstract":"PURPOSE OF REVIEW Smoking is a major risk factor for lung cancer, which is the leading cause of cancer-related deaths both in the USA and worldwide. Chronic obstructive pulmonary disease and emphysema are comorbid conditions often found in lung cancer patients. The inflammatory pathways that link chronic obstructive pulmonary disease, emphysema, and lung cancer likely involve genetic and epigenetic modulations due to chronic tissue injury and abnormal tumor immunity in susceptible hosts. RECENT FINDINGS Chronic airway inflammation contributes to alterations in the bronchial epithelium and lung microenvironment, provoking a milieu conducive to pulmonary carcinogenesis. For example, inflammation-inducible cyclooxygenase-2 is upregulated in nonsmall cell lung cancer and also plays an important role in promoting epithelial-to-mesenchymal transition. Genetic changes in the airway epithelium of smokers may help predict or identify individuals at risk for lung cancer. Finally, radiographic findings of emphysema have been established as independent risk factors for lung cancer. SUMMARY The relationships between inflammation, airflow obstruction, and lung cancer are complex. Deregulated inflammation is complicit in the pathogenesis of chronic obstructive pulmonary disease and lung cancer, but the overlap of signaling events is not yet fully understood. Tobacco exposure is an important risk factor that confers long-term risk of lung disease. Diagnostic sensitivity of detecting lung cancer may improve with the utilization of genetic profiling in combination with pathologic evaluation of airway epithelium. Additional research is required to understand the role of epithelial-to-mesenchymal transition in chronic inflammatory lung diseases and lung carcinogenesis.","author":[{"dropping-particle":"","family":"Lee","given":"Gina","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Walser","given":"Tonya C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dubinett","given":"Steven M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Current opinion in pulmonary medicine","id":"ITEM-2","issue":"4","issued":{"date-parts":[["2009","7"]]},"page":"303-7","title":"Chronic inflammation, chronic obstructive pulmonary disease, and lung cancer.","type":"article-journal","volume":"15"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.5306/wjco.v5.i4.660","ISSN":"2218-4333","PMID":"25300704","abstract":"The association between chronic obstructive pulmonary disease (COPD) and lung cancer has long been a subject of intense debate. The high prevalence of COPD in elderly smokers inevitably strengthens their coincidence. In addition to this contingent coincidence, recent studies have revealed a close association between the two diseases that is independent of the smoking history; that is, the existence of COPD is an independent risk factor for the development of lung cancer. Molecular-based evidence has been accumulating as a result of the efforts to explain the underlying mechanisms of this association. These mechanisms may include the following: the retention of airborne carcinogens followed by the activation of oncogenes and the suppression of tumor suppressor genes; the complex molecular mechanism associated with chronic inflammation in the distal airways of patients with COPD; the possible involvement of putative distal airway stem cells; and genetic factors that are common to both COPD and lung cancer. The existence of COPD in patients with lung cancer may potentially affect the process of diagnosis, surgical resection, radiotherapy, chemotherapy, and end-of-life care. The comprehensive management of COPD is extremely important for the appropriate treatment of lung cancer. Surgical resections with the aid of early interventions for COPD are often possible, even for patients with mild-to-moderate COPD. New challenges, such as lung cancer CT screening for individuals at high risk, are now in the process of being implemented. Evaluating the risk of lung cancer in patients with COPD may be warranted in community-based lung cancer screening.","author":[{"dropping-particle":"","family":"Takiguchi","given":"Yuichi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sekine","given":"Ikuo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Iwasawa","given":"Shunichiro","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kurimoto","given":"Ryota","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tatsumi","given":"Koichiro","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World journal of clinical oncology","id":"ITEM-3","issue":"4","issued":{"date-parts":[["2014","10","10"]]},"page":"660-6","title":"Chronic obstructive pulmonary disease as a risk factor for lung cancer.","type":"article-journal","volume":"5"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1016/j.semcancer.2016.11.001","ISSN":"1096-3650","PMID":"27840275","abstract":"Genetic variations in COPD and lung cancer may be one of the molecular mechanisms responsible for COPD-lung cancer transformation. The present review highlights main genetic variations co-existed in COPD and lung cancer and integrates the varied genes into four molecular mechanisms, e.g. activated cell proliferation pathway, tumor suppressor and DNA repair gene dysfunction, chronic inflammatory microenvironment, and impaired immune response, by which COPD epithelial cells may be transformed into tumorigenic status. We call special attention to further investigate the transformation from COPD to lung cancer and understand the exact molecular mechanisms, to prevent and reduce the increased incidence of COPD and lung cancer. We call direct evidence to show the existence of COPD-lung cancer transformation, since current evidence to support the transformation from COPD to lung cancer is the co-existence of selected genes and proteins in both. We should furthermore explore and understand the homogeneity and heterogeneity of gene mutation, epigenetics, sequencing, and function between COPD and lung cancer. The defining and understanding of COPD-lung cancer transformation will provide new diagnostic and therapeutic biomarkers as a new milestone to prevent and treat lung cancer.","author":[{"dropping-particle":"","family":"Wang","given":"Diane C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shi","given":"Lin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhu","given":"Zhenhua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gao","given":"Danyan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Yong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Seminars in cancer biology","id":"ITEM-4","issued":{"date-parts":[["2017"]]},"page":"52-59","title":"Genomic mechanisms of transformation from chronic obstructive pulmonary disease to lung cancer.","type":"article-journal","volume":"42"},"uris":[""]}],"mendeley":{"formattedCitation":"^{97,100,102,103}","plainTextFormattedCitation":"97,100,102,103","previouslyFormattedCitation":"^{96,99,101,102}"},"properties":{"noteIndex":0},"schema":""}97,100,102,103ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.pupt.2013.05.003","ISSN":"1522-9629","PMID":"23701918","abstract":"Chronic obstructive pulmonary disease (COPD) affects more than 200 million people worldwide and is expected to become the third leading cause of death in 2020. COPD is characterized by progressive airflow limitation, due to a combination of chronic inflammation and remodeling of the small airways (bronchiolitis) and loss of elastic recoil caused by destruction of the alveolar walls (emphysema). Lung cancer is the most important cause of cancer-related death in the world. (Cigarette) smoking is the principal culprit causing both COPD and lung cancer; in addition, exposure to environmental tobacco smoke, biomass fuel smoke, coal smoke and outdoor air pollution have also been associated with an increased incidence of both diseases. Importantly, smokers with COPD--defined as either not fully reversible airflow limitation or emphysema--have a two- to four-fold increased risk to develop lung cancer. In this review, we highlight several of the genetic, epigenetic and inflammatory mechanisms, which link COPD and carcinogenesis in the lungs. Elucidating the biological pathways and networks, which underlie the increased susceptibility of lung cancer in patients with COPD, has important implications for screening, prevention, diagnosis and treatment of these two devastating pulmonary diseases.","author":[{"dropping-particle":"","family":"Vermaelen","given":"K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brusselle","given":"G","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Pulmonary pharmacology & therapeutics","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2013","10"]]},"page":"544-54","title":"Exposing a deadly alliance: novel insights into the biological links between COPD and lung cancer.","type":"article-journal","volume":"26"},"uris":[""]}],"mendeley":{"formattedCitation":"⁹⁹","plainTextFormattedCitation":"99","previouslyFormattedCitation":"⁹⁸"},"properties":{"noteIndex":0},"schema":""}99. Further identification of genetic loci or pathways underlying this overlap is needed to understand underlying mechanism of this processADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1111/j.1440-1843.2011.02019.x","ISSN":"1440-1843","PMID":"21749550","abstract":"Over the last 30years, epidemiological studies have shown that COPD is the single most important risk factor for lung cancer after smoking exposure. Recent genetic studies using genome-wide approaches suggest that the genetic risk factors predisposing smokers to COPD and lung cancer may overlap. The genes identified by these studies suggest that this overlapping genetic susceptibility may be mediated through receptors expressed on the bronchial epithelium that implicate molecular pathways underlying both COPD and lung cancer. Furthermore, it appears that aberrant inflammatory and/or immune-modulatory pathways leading to excess matrix metalloproteinases, growth factors and airway remodelling in COPD may also be promoting malignant transformation of the bronchial epithelium. The process linking inflammation, remodelling and cancer formation is called epithelial-mesenchymal transition. There are several clinical implications arising from the COPD-lung cancer overlap. First, if COPD is a precursor disease to lung cancer then efforts to prevent COPD, might be even more important. Second, if drugs targeting the overlapping molecular pathways can be identified, chemoprevention that reduce the propensity to COPD and lung cancer is an attractive option. Finally, if low-dose computerized tomography can identify treatable lung cancer, gene-based tests of susceptibility might help identify those smokers who should undergo radiological screening.","author":[{"dropping-particle":"","family":"Young","given":"Robert P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hopkins","given":"Raewyn J","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Respirology (Carlton, Vic.)","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2011","10"]]},"page":"1047-55","title":"How the genetics of lung cancer may overlap with COPD.","type":"article-journal","volume":"16"},"uris":[""]}],"mendeley":{"formattedCitation":"¹⁰⁴","plainTextFormattedCitation":"104","previouslyFormattedCitation":"¹⁰³"},"properties":{"noteIndex":0},"schema":""}104.While our study is the largest genome-wide study of COPD, our use of population-based lung function for replication, along with pre-bronchodilator spirometry, could bias our findings against variants that are only associated with more severe forms of COPD. Based on prior analysesADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/ng.3752","ISBN":"1546-1718 (Electronic)\r1061-4036 (Linking)","ISSN":"1546-1718","PMID":"28166215","abstract":"Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. We performed a genetic association study in 15,256 cases and 47,936 controls, with replication of select top results (P < 5 x 10-6) in 9,498 cases and 9,748 controls. In the combined meta-analysis, we identified 22 loci associated at genome-wide significance, including 13 new associations with COPD. Nine of these 13 loci have been associated with lung function in general population samples, while 4 (EEFSEC, DSP, MTCL1, and SFTPD) are new. We noted two loci shared with pulmonary fibrosis (FAM13A and DSP) but that had opposite risk alleles for COPD. None of our loci overlapped with genome-wide associations for asthma, although one locus has been implicated in joint susceptibility to asthma and obesity. We also identified genetic correlation between COPD and asthma. Our findings highlight new loci associated with COPD, demonstrate the importance of specific loci associated with lung function to COPD, and identify potential regions of genetic overlap between COPD and other respiratory diseases.","author":[{"dropping-particle":"","family":"Hobbs","given":"Brian D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jong","given":"Kim","non-dropping-particle":"de","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lamontagne","given":"Maxime","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bosse","given":"Y","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Shrine","given":"Nick","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Artigas","given":"María 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Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Paré, Peter D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; COPDGene Investigators; ECLIPSE Investigators; LifeLines Investigators; SPIROMICS Research Group; International COPD Genetics Network Investigators; UK BiLEVE Investigators; International COPD Genetics Consortium; Bosse, Y; Shrine, Nick; Artigas, María Soler; Wain, Louise V; Hall, Ian P; Jackson, Victoria E; Wyss, Annah B; London, Stephanie J; North, Kari E; Franceschini, Nora; Strachan, David P; Beaty, Terri H; Hokanson, John E; Crapo, James D; Castaldi, Peter J; Chase, Robert P; Bartz, Traci M; Heckbert, Susan R; Psaty, Bruce M; Gharib, Sina A; Zanen, Pieter; Lammers, Jan W; Oudkerk, Matthijs; Groen, H J; Locantore, Nicholas; Tal-Singer, Ruth; Rennard, Stephen I; Vestbo, J?rgen; Timens, Wim; Pare, P D; Latourelle, Jeanne C; Dupuis, Josée; O'Connor, George T; Wilk, Jemma B; Kim, Woo Jin; Lee, Mi Kyeong; Oh, Yeon-Mok M; Vonk, Judith M; de Koning, Harry J; Leng, Shuguang; Belinsky, Steven A; Tesfaigzi, Yohannes; Manichaikul, Ani; Wang, Xin-Qun Q; Rich, Stephen S; Barr, R Graham; Sparrow, David; Litonjua, Augusto A; Bakke, Per; Gulsvik, Amund; Lahousse, Lies; Brusselle, Guy G; Stricker, Bruno H; Uitterlinden, André G; Ampleford, Elizabeth J; Bleecker, Eugene R; Woodruff, Prescott G; Meyers, Deborah A; Qiao, Dandi; Lomas, David A; Yim, Jae-Joon J; Kim, Deog Kyeom; Hawrylkiewicz, Iwona; Sliwinski, Pawel; Hardin, Megan; Fingerlin, Tasha E; Schwartz, David A; Postma, Dirkje S; MacNee, William; Tobin, Martin D; Silverman, Edwin K; Boezen, H Marike; Cho, Michael H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Consortium Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium Network Investigators)\n\nFrom Duplicate 1 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.\n\nFrom Duplicate 2 (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis - Hobbs, B D; de Jong, K; Lamontagne, M; Bosse, Y; Shrine, N; Artigas, M S; Wain, L V; Hall, I P; Jackson, V E; Wyss, A B; London, S J; North, K E; Franceschini, N; Strachan, D P; Beaty, T H; Hokanson, J E; Crapo, J D; Castaldi, P J; Chase, R P; Bartz, T M; Heckbert, S R; Psaty, B M; Gharib, S A; Zanen, P; Lammers, J W; Oudkerk, M; Groen, H J; Locantore, N; Tal-Singer, R; Rennard, S I; Vestbo, J; Timens, W; Pare, P D; Latourelle, J C; Dupuis, J; O'Connor, G T; Wilk, J B; Kim, W J; Lee, M K; Oh, Y M; Vonk, J M; de Koning, H J; Leng, S; Belinsky, S A; Tesfaigzi, Y; Manichaikul, A; Wang, X Q; Rich, S S; Barr, R G; Sparrow, D; Litonjua, A A; Bakke, P; Gulsvik, A; Lahousse, L; Brusselle, G G; Stricker, B H; Uitterlinden, A G; Ampleford, E J; Bleecker, E R; Woodruff, P G; Meyers, D A; Qiao, D; Lomas, D A; Yim, J J; Kim, D K; Hawrylkiewicz, I; Sliwinski, P; Hardin, M; Fingerlin, T E; Schwartz, D A; Postma, D S; MacNee, W; Tobin, M D; Silverman, E K; Boezen, H M; Cho, M H; Investigators, C OPDGene; Investigators, Eclipse; LifeLines, Investigators; Group, Spiromics Research; International, Copd Genetics Network Investigators; Investigators, U K BiLEVE; International, Copd Genetics Consortium)\n\nHobbs, Brian D\nde Jong, Kim\nLamontagne, Maxime\nBosse, Yohan\nShrine, Nick\nArtigas, Maria Soler\nWain, Louise V\nHall, Ian P\nJackson, Victoria E\nWyss, Annah B\nLondon, Stephanie J\nNorth, Kari E\nFranceschini, Nora\nStrachan, David P\nBeaty, Terri H\nHokanson, John E\nCrapo, James D\nCastaldi, Peter J\nChase, Robert P\nBartz, Traci M\nHeckbert, Susan R\nPsaty, Bruce M\nGharib, Sina A\nZanen, Pieter\nLammers, Jan W\nOudkerk, Matthijs\nGroen, H J\nLocantore, Nicholas\nTal-Singer, Ruth\nRennard, Stephen I\nVestbo, Jorgen\nTimens, Wim\nPare, Peter D\nLatourelle, Jeanne C\nDupuis, Josee\nO'Connor, George T\nWilk, Jemma B\nKim, Woo Jin\nLee, Mi Kyeong\nOh, Yeon-Mok\nVonk, Judith M\nde Koning, Harry J\nLeng, Shuguang\nBelinsky, Steven A\nTesfaigzi, Yohannes\nManichaikul, Ani\nWang, Xin-Qun\nRich, Stephen S\nBarr, R Graham\nSparrow, David\nLitonjua, Augusto A\nBakke, Per\nGulsvik, Amund\nLahousse, Lies\nBrusselle, Guy G\nStricker, Bruno H\nUitterlinden, Andre G\nAmpleford, Elizabeth J\nBleecker, Eugene R\nWoodruff, Prescott G\nMeyers, Deborah A\nQiao, Dandi\nLomas, David A\nYim, Jae-Joon\nKim, Deog Kyeom\nHawrylkiewicz, Iwona\nSliwinski, Pawel\nHardin, Megan\nFingerlin, Tasha E\nSchwartz, David A\nPostma, Dirkje S\nMacNee, William\nTobin, Martin D\nSilverman, Edwin K\nBoezen, H Marike\nCho, Michael H\neng\nR01 HL113264/HL/NHLBI NIH HHS/\nR01 HL089897/HL/NHLBI NIH HHS/\nK08 HL097029/HL/NHLBI NIH HHS/\nR01 HL075478/HL/NHLBI NIH HHS/\nR01 HL089856/HL/NHLBI NIH HHS/\nP01 HL105339/HL/NHLBI NIH HHS/\nR01 HL126596/HL/NHLBI NIH HHS/\nK01 HL129039/HL/NHLBI NIH HHS/\nG0902313/Medical Research Council/United Kingdom\nR01 HL124233/HL/NHLBI NIH HHS/\nR01 HL084323/HL/NHLBI NIH HHS/\nP01 HL114501/HL/NHLBI NIH HHS/\nT32 HL007427/HL/NHLBI NIH HHS/\n2017/02/07 06:00\nNat Genet. 2017 Mar;49(3):426-432. doi: 10.1038/ng.3752. Epub 2017 Feb 6.","page":"426-432","title":"Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis","type":"article-journal","volume":"49"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵","plainTextFormattedCitation":"5","previouslyFormattedCitation":"⁵"},"properties":{"noteIndex":0},"schema":""}5, we believe that in most cases this bias is small. However, we cannot rule out a role for studying more severe disease; we note that the alpha-1 locus (SERPINA1) was identified as genome-wide significant in smaller studies of emphysema and in severe COPD in smokers. Power considerations in genome-wide association studies generally favor larger sample sizes and are relatively tolerant of phenotypic misclassification. Whether other subsets of subjects with COPD of sufficient phenotypic specificity and sample size can lead to identification of new loci remains to be determined. The largest sample size came from the UK Biobank; using UK Biobank as the test and the ICGC as the replication did not identify novel replicated genome-wide significant findings after Bonferroni correction. Our study focused on relatively common variants available through HRC imputation; additional more detailed studies of rare variants, regions such as HLA, and importantly, in other ethnicities, are warranted, but the latter, in particular are limited by currently available cohorts.In conclusion, our work finds a substantial number of new loci for COPD, and via extensive bioinformatic analysis identifies potential genes and pathways for both existing and novel loci. Characterization of phenotypic effects of these loci suggests heterogeneity across COPD-associated loci as well as new findings of overlap with asthma and idiopathic pulmonary fibrosis. As the burden of COPD continues to increase, our study provides multiple avenues for new investigation for this deadly disease.FiguresFigure 1 Study designCOPD, chronic obstructive pulmonary disease; FEV1, force expiratory volume in one second; FVC, forced vital capacity; ICGC, International COPD Genetics Consortium.Figure 2 Manhattan plotLoci are labeled with the closest gene to the lead variant. Variants within 500 kb of the top associated variant at each novel locus are in indicated in orange.Figure 3 Heatmap of associations of 82 index variants and imaging phenotypes in COPDGene. White lines represent putative groups of emphysema (bottom left), gas trapping (upper right), and a mixed group.Figure 4 Shared genomic segments between COPD with asthma and idiopathic pulmonary fibrosisShared genomic segments between COPD with A) asthma and B) idiopathic pulmonary fibrosis. Genomic segments with posterior probability of association (PPA) greater than 0.6 are labeled.TablesTable 1 Meta-analysis results showing 35 loci novel for COPD and lung function(see the Excel file)ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1GBD 2015 Chronic Respiratory Disease Collaborators. Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Respir Med 2017; 5: 691–706.2Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature 2016; 536: 41–7.3Jiang Z, Lao T, Qiu W, et al. A Chronic Obstructive Pulmonary Disease Susceptibility Gene, FAM13A, Regulates Protein Stability of beta-Catenin. Am J Respir Crit Care Med 2016; 194: 185–97.4Lao T, Jiang Z, Yun J, et al. Hhip haploinsufficiency sensitizes mice to age-related emphysema. 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