Advanced Population and Medical Genetics
EPI511, Spring 1, 2016 [Jan 26 – Mar 10]
Tue/Thu 1:30-3:20pm, FXB G03
Instructor
Alkes Price, Associate Professor (aprice@hsph.harvard.edu)
Office Hours: Fri 2:30-3:30pm, HSPH Building 2, Room 211 [Jan 29 – Mar 11]
Teaching Assistant
Kevin Galinsky, Doctoral student (galinsky@fas.harvard.edu)
Office Hours: Fri 10:30-11:30am + Mon 10:30-11:30am, Kresge 120 [Jan 29 – 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 written in Python (iPython notebook format preferred) or PERL, and its output,
are to be submitted via the Experiences dropbox on the course www site.
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 26
Introduction + HapMap /
International HapMap3 Consortium 2010 Nature1
1000 Genomes projects
(Price et al. 2015 Proc R Sci B2)
Thu Jan 28
Linkage disequilibrium
Slatkin 2008 Nat Rev Genet3
(Conrad et al. 2006 Nat Genet4)
Tue Feb 2
Population structure
Novembre et al. 2008 Nature5
(Tishkoff et al. 2009 Science 6)
Thu Feb 4
Population admixture
Sankararaman et al. 2008 Am J Hum Genet7
(Price et al. 2009 PLoS Genet8)
Tue Feb 9
Population stratification 1:
Campbell et al. 2005 Nat Genet9
Genomic Control; …
(Devlin & Roeder 1999 Biometrics10)
Thu Feb 11 Population stratification 2:
Price et al. 2006 Nat Genet11
PCA; …
(Kang et al. 2010 Nat Genet12)
Tue Feb 16 Admixture mapping
Freedman et al. 2006 PNAS13
(Seldin et al. 2011 Nat Rev Genet14)
Thu Feb 18 Fine-mapping
Maller et al. 2012 Nat Genet15
(Haiman et al. 2007 Nat Genet16)
Tue Feb 23 Natural selection 1:
Sabeti et al. 2006 Science17
Population differentiation; … (Galinsky et al. 2016 Am J Hum Genet18)
Thu Feb 25 Natural selection 2:
Sankararaman et al. 2014 Nature19
Ancient DNA*
(Vernot & Akey 2014 Science20)
Tue Mar 1
Heritability
Yang et al. 2010 Nat Genet21
(Gibson 2012 Nat Rev Genet22)
Thu Mar 3
Genetic risk prediction
Purcell et al. 2009 Nature23
(Vilhjalmsson et al. 2015 Am J Hum Genet24)
Tue Mar 8
Mixed model association
Yang et al. 2014 Nat Genet25
(Loh et al. 2015 Nat Genet26)
Thu Mar 10 Functional interpretation of
Finucane et al. 2015 Nat Genet27
genetic associations
(Trynka et al. 2013 Nat Genet28)
*guest lecture by David Reich, Professor of Genetics, Harvard Medical School
Experiences (due by 8:00am each Tuesday. Please send by email to TA Kevin Galinsky.)
Experience 1: due Tue Feb 2
Experience 2: due Tue Feb 9
Experience 3: due Tue Feb 16
Experience 4: due Tue Feb 23
Experience 5: due Tue Mar 1
Experience 6: due Tue Mar 8
Research Paper (due by 5:00pm Fri Mar 11. 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 23.
Each student should schedule a 20-minute appointment with the instructor during the week of
Feb 29 – Mar 4, and should choose and begin work on their topic prior to this meeting. The
short research paper will be due on Fri Mar 11. 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
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The International HapMap 3 Consortium. An integrated haplotype map of rare and common
genetic variation in diverse human populations. Nature 467, 52-8 (2010).
Price, A.L., Spencer, C.C. & Donnelly, P. Progress and promise in understanding the genetic
basis of common diseases. Proc R Sci B 282, 20151684 (2015).
Slatkin, M. Linkage disequilibrium--understanding the evolutionary past and mapping the medical
future. Nat Rev Genet 9, 477-85 (2008).
Conrad, D.F. et al. A worldwide survey of haplotype variation and linkage disequilibrium in the
human genome. Nat Genet 38, 1251-60 (2006).
Novembre, J. et al. Genes mirror geography within Europe. Nature 456, 98-101 (2008).
Tishkoff, S.A. et al. The genetic structure and history of Africans and African Americans. Science
324, 1035-44 (2009).
Sankararaman, S., Sridhar, S., Kimmel, G. & Halperin, E. Estimating local ancestry in admixed
populations. Am J Hum Genet 82, 290-303 (2008).
Price, A.L. et al. Sensitive detection of chromosomal segments of distinct ancestry in admixed
populations. PLoS Genet 5, e1000519 (2009).
Campbell, C.D. et al. Demonstrating stratification in a European American population. Nat Genet
37, 868-72 (2005).
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997-1004 (1999).
Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide
association studies. Nat Genet 38, 904-9 (2006).
Kang, H.M. et al. Variance component model to account for sample structure in genome-wide
association studies. Nat Genet 42, 348-54 (2010).
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).
Seldin, M.F., Pasaniuc, B. & Price, A.L. New approaches to disease mapping in admixed
populations. Nat Rev Genet (2011).
Wellcome Trust Case Control, C. et al. Bayesian refinement of association signals for 14 loci in 3
common diseases. Nat Genet 44, 1294-301 (2012).
Haiman, C.A. et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nat
Genet 39, 638-44 (2007).
Sabeti, P.C. et al. Positive natural selection in the human lineage. Science 312, 1614-20 (2006).
Galinsky, K.J. et al. Fast principal components analysis reveals convergent evolution of ADH1B
gene in Europe and East Asia. Am J Hum Genet (2016).
Sankararaman, S. et al. The genomic landscape of Neanderthal ancestry in present-day humans.
Nature 507, 354-7 (2014).
Vernot, B. & Akey, J.M. Resurrecting surviving Neandertal lineages from modern human
genomes. Science 343, 1017-21 (2014).
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat
Genet 42, 565-9 (2010).
Gibson, G. Rare and common variants: twenty arguments. Nat Rev Genet 13, 135-45 (2011).
Purcell, S.M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar
disorder. Nature 460, 748-52 (2009).
Vilhjalmsson, B.J. et al. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk
Scores. Am J Hum Genet 97, 576-92 (2015).
Yang, J., Zaitlen, N.A, Goddard, M.E., Visscher, P.M. & Price, A.L. Advantages and pitfalls in the
application of mixed-model association methods. Nat Genet 46, 100-106 (2014).
Loh, P.R. et al. Efficient Bayesian mixed-model analysis increases association power in large
cohorts. Nat Genet 47, 284-90 (2015).
Finucane, H.K. et al. Partitioning heritability by functional category using GWAS summary
statistics. Nat Genet 47, 1228-35 (2015).
Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait
variants. Nat Genet 45, 124-30 (2013).
Lindstrom, S. et al. Common variants in ZNF365 are associated with both mammographic density
and breast cancer risk. Nat Genet 43, 185-7 (2011).