Theory and Practice of Public Health in the United States



Advanced Population and Medical Genetics

EPI511, Spring 1, 2014 [Jan 28 – Mar 13]

Tue/Thu 8:30-10:20am, FXB G10

Instructor

Alkes Price, Assistant Professor (aprice@hsph.harvard.edu)

Office Hours: Thu 2:30-3:30pm, HSPH Building 2, Room 211 [Jan 30 – Mar 13]

Teaching Assistant

Tristan Hayeck, Doctoral student (thayeck@hsph.harvard.edu)

Weekly Problem Session: Thu 10:30-11:30am, FXB G10 [Jan 30 – Mar 6]

Office Hours: Fri 10:30-11:30am, HSPH Building 2, Room 249A [Jan 31 – Mar 7]

Prerequisites

• BIO510 or equivalent programming experience in Python or PERL

• BIO227 or EPI507 or EPI293 or equivalent experience in genetics

Course Description

This course will cover quantitative topics in human population genetics and applications to medical genetics, including the HapMap project, linkage disequilibrium, population structure and stratification, population admixture, admixture mapping, heritability and genetic risk prediction. The course is aimed at Epidemiology and Biostatistics students with a strong interest in statistical genetics, and is included in the Biostatistics Advanced Doctoral Core and Biostatistics Master’s core. The course will emphasize hands-on analysis of large empirical data sets, thus requiring prior experience with a general-purpose high-level programming language such as Python or PERL. After taking this course, each student will have the experience and skills to develop and apply statistical methods to population genetic data.

Course Objectives

After taking this course, the student will be able to:

• Critically analyze large empirical data sets using a high-level programming language such as Python or PERL.

• Apply fundamental concepts in population and medical genetics such as linkage disequilibrium, population structure and stratification, population admixture, admixture mapping, and heritability.

• Develop and apply statistical methods to population genetic data.

Texts and Reading Materials

Lecture notes and links to relevant scientific papers will be provided on the course website.

Outcome Measures and Grading

At the heart of this course are 6 Experiences: weekly take-home projects in which students apply fundamental concepts from the reading, lecture and discussion parts of the course to analyze empirical data sets. The Experiences will determine 60% of the grade for this course. Computer code (Python or PERL) and the output it produces are to be submitted by email.

In addition, each student will write a short research paper (1,000-1,500 words) describing their scientific results on a project of their choice. A list of suggested project topics will be provided. The short research paper will determine 40% of the grade for this course.

Course Evaluations

Completion of the evaluation is a requirement for each course.  Your grade will not be available until you submit the evaluation.  In addition, registration for future terms will be blocked until you have completed evaluations for courses in prior terms.

Course Schedule

|Date |Topic |Required advance reading |

| | |(Optional advance reading in parentheses) |

|Tue Jan 28 |Introduction + HapMap / 1000 Genomes |Rosenberg et al. 2010 Nat Rev Genet1 |

| |projects |(International HapMap3 Consortium 2010 Nature2) |

|Thu Jan 30 |Linkage disequilibrium |Slatkin 2008 Nat Rev Genet3 |

| | |(Conrad et al. 2006 Nat Genet4) |

|Tue Feb 4 |Population structure 1 |Rosenberg et al. 2002 Science5 |

| | |(Tishkoff et al. 2009 Science 6) |

|Thu Feb 6 |Population structure 2 |Novembre et al. 2008 Nature7 |

| | |(Cavalli-Sforza et al. 1993 Science8) |

|Tue Feb 11 |Population stratification 1 |Campbell et al. 2005 Nat Genet9 |

| | |(Devlin & Roeder 1999 Biometrics10) |

|Thu Feb 13 |Population stratification 2 |Price et al. 2006 Nat Genet11 |

| | |(Kang et al. 2010 Nat Genet12) |

|Tue Feb 18 |Population admixture |Sankararaman et al. 2008 Am J Hum Genet13 |

| | |(Price et al. 2009 PLoS Genet14) |

|Thu Feb 20 |Admixture mapping |Freedman et al. 2006 PNAS15 |

| | |(Seldin et al. 2011 Nat Rev Genet16) |

|Tue Feb 25 |Fine-mapping |Maller et al. 2012 Nat Genet17 |

| | |(Haiman et al. 2007 Nat Genet18) |

|Thu Feb 27 |Natural selection |Sabeti et al. 2006 Science19 |

| | |(Grossman et al. 2013 Cell20) |

|Tue Mar 4 |Heritability |Yang et al. 2010 Nat Genet21 |

| | |(Gibson 2012 Nat Rev Genet22) |

|Thu Mar 6 |Genetic risk prediction |Purcell et al. 2009 Nature23 |

| | |(Chatterjee et al. 2013 Nat Genet24) |

|Tue Mar 11 |Mixed model association |Yang et al. 2014 Nat Genet25 |

| | |(Listgarten et al. 2012 Nat Methods26) |

|Thu Mar 13 |Functional interpretation of genetic |Trynka et al. 2013 Nat Genet27 |

| |associations* |(McVicker et al. 2013 Science28) |

*guest lecture by Soumya Raychaudhuri, Brigham & Women’s Hospital / Harvard Medical School

Experiences (due by 8:00am each Tuesday. Please send by email to Tristan Hayeck.)

Experience 1: due Tue Feb 4

Experience 2: due Tue Feb 11

Experience 3: due Tue Feb 18

Experience 4: due Tue Feb 25

Experience 5: due Tue Mar 4

Experience 6: due Tue Mar 11

Research Paper (due by 5:00pm Fri Mar 14. Please send by email to Alkes Price.)

Each student should choose one topic for the short research paper. Possible topics will be suggested each week, and an aggregate list of suggested topics will be provided on Feb 25. Each student should schedule a 30-minute appointment with the instructor during the week of Mar 3 – Mar 7, and should choose and begin work on their topic prior to this meeting. The short research paper will be due on Fri Mar 14. The paper should be 1,000-1,500 words long, and should include an abstract, plus one figure and one table and at least 10 references. Additional subdivision into Introduction, Results, Discussion and Methods sections is optional. For an example of a short research paper, see Lindstrom et al. 2011 Nat Genet29.

Bibliography

1. Rosenberg, N.A. et al. Genome-wide association studies in diverse populations. Nat Rev Genet 11, 356-66 (2010).

2. The International HapMap 3 Consortium. An integrated haplotype map of rare and common genetic variation in diverse human populations. Nature 467, 52-8 (2010).

3. Slatkin, M. Linkage disequilibrium--understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9, 477-85 (2008).

4. Conrad, D.F. et al. A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nat Genet 38, 1251-60 (2006).

5. Rosenberg, N.A. et al. Genetic structure of human populations. Science 298, 2381-5 (2002).

6. Tishkoff, S.A. et al. The genetic structure and history of Africans and African Americans. Science 324, 1035-44 (2009).

7. Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98-101 (2008).

8. Cavalli-Sforza, L.L., Menozzi, P. & Piazza, A. Demic expansions and human evolution. Science 259, 639-46 (1993).

9. Campbell, C.D. et al. Demonstrating stratification in a European American population. Nat Genet 37, 868-72 (2005).

10. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997-1004 (1999).

11. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904-9 (2006).

12. Kang, H.M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat Genet 42, 348-54 (2010).

13. Sankararaman, S., Sridhar, S., Kimmel, G. & Halperin, E. Estimating local ancestry in admixed populations. Am J Hum Genet 82, 290-303 (2008).

14. Price, A.L. et al. Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet 5, e1000519 (2009).

15. Freedman, M.L. et al. Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men. Proc Natl Acad Sci U S A 103, 14068-73 (2006).

16. Seldin, M.F., Pasaniuc, B. & Price, A.L. New approaches to disease mapping in admixed populations. Nat Rev Genet (2011).

17. Maller, J.B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat Genet 44, 1294-301 (2012).

18. Haiman, C.A. et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nat Genet 39, 638-44 (2007).

19. Sabeti, P.C. et al. Positive natural selection in the human lineage. Science 312, 1614-20 (2006).

20. Grossman, S.R. et al. Identifying recent adaptations in large-scale genomic data. Cell 152, 703-13 (2013).

21. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42, 565-9 (2010).

22. Gibson, G. Rare and common variants: twenty arguments. Nat Rev Genet 13, 135-45 (2011).

23. Purcell, S.M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748-52 (2009).

24. Chatterjee, N. et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet 45, 400-5, 405e1-3 (2013).

25. Yang, J. et al. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet (2014).

26. Listgarten, J. et al. Improved linear mixed models for genome-wide association studies. Nat Methods 9, 525-6 (2012).

27. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 45, 124-30 (2013).

28. McVicker, G. et al. Identification of genetic variants that affect histone modifications in human cells. Science 342, 747-9 (2013).

29. Lindstrom, S. et al. Common variants in ZNF365 are associated with both mammographic density and breast cancer risk. Nat Genet 43, 185-7 (2011).

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