Genome-wide association studies (GWAS) have been successful in identifying common genetic variants contributing to disease risk. However, nearly all of these studies have been conducted in populations of European ancestry. It is important to include other populations, because GWAS in Europeans are unlikely to detect risk variants that are common only in non-European populations. In the United States, the majority of individuals of non-European ancestry belong to admixed populations (e.g. African Americans or Latinos) that inherit ancestry from more than one continental population. Existing GWAS methods for admixed populations are inadequate, because they do not incorporate both SNP association and admixture association signals. Thus, if existing methods are applied, analyses will not be fully powered and important variants will be missed. For diseases with known population differences-such as cardiovascular disease in African Americans and asthma in Latinos-the need to develop methods that combine these signals is particularly pressing, because admixture association signals are likely to be particularly important. Here, we propose to develop a complete set of methods and software to combine SNP and admixture association signals in GWAS in admixed populations, while addressing questions such as imputation and choosing SNPs for replication. Our goal is to make fully powered association studies in populations of mixed ancestry as practical as studies in populations of homogeneous ancestry. In addition to African Americans, we will also develop methods for complex admixed populations (e.g. Latinos) that inherit ancestry from three or more continental populations, and for related individuals from admixed populations. Our methods research will be driven by empirical data, including over 10,000 African American samples and 2,400 Latino samples that will be genome-scanned by the CARe consortium, the Jackson Heart Study, and the Multiethnic Cohort Study. Our work will be applicable not only to GWAS in admixed populations, but also to meta-analyses of European and admixed populations, as well as future resequencing-based studies.

Public Health Relevance

Genome-wide association studies (GWAS), an approach in which the genomes of both diseased and healthy individuals are scanned to identify genes affecting disease risk, have thus far been primarily restricted to populations of European ancestry. It is important to extend these studies to other populations, such as admixed populations (African Americans and Latinos) that inherit ancestry from multiple continental groups, but existing statistical methods for conducting GWAS in admixed populations are inadequate due to the complexities posed by chromosomal segments of distinct continental ancestry. In this proposal, we will use empirical genetic data sets to develop statistical methods and software to fill this gap.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG006399-03
Application #
8464181
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Brooks, Lisa
Project Start
2011-06-15
Project End
2016-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
3
Fiscal Year
2013
Total Cost
$491,824
Indirect Cost
$165,251
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Loh, Po-Ru; Danecek, Petr; Palamara, Pier Francesco et al. (2016) Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet 48:1443-1448
Brown, Brielin C; Asian Genetic Epidemiology Network Type 2 Diabetes Consortium; Ye, Chun Jimmie et al. (2016) Transethnic Genetic-Correlation Estimates from Summary Statistics. Am J Hum Genet 99:76-88
Loh, Po-Ru; Palamara, Pier Francesco; Price, Alkes L (2016) Fast and accurate long-range phasing in a UK Biobank cohort. Nat Genet 48:811-6
Galinsky, Kevin J; Loh, Po-Ru; Mallick, Swapan et al. (2016) Population Structure of UK Biobank and Ancient Eurasians Reveals Adaptation at Genes Influencing Blood Pressure. Am J Hum Genet 99:1130-1139
Gymrek, Melissa; Willems, Thomas; Guilmatre, Audrey et al. (2016) Abundant contribution of short tandem repeats to gene expression variation in humans. Nat Genet 48:22-9
Galinsky, Kevin J; Bhatia, Gaurav; Loh, Po-Ru et al. (2016) Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet 98:456-72
Loh, Po-Ru; Bhatia, Gaurav; Gusev, Alexander et al. (2015) Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat Genet 47:1385-92
Loh, Po-Ru; Tucker, George; Bulik-Sullivan, Brendan K et al. (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 47:284-90
Chen, Chia-Yen; Han, Jiali; Hunter, David J et al. (2015) Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction. Genet Epidemiol 39:427-38
Price, Alkes L; Spencer, Chris C A; Donnelly, Peter (2015) Progress and promise in understanding the genetic basis of common diseases. Proc Biol Sci 282:20151684

Showing the most recent 10 out of 51 publications