BST 290: Hua Tang

Biostatistics Seminar: Hua Tang

DATE: Tuesday, March 11th, 2014
TIME: 4:10pm (refreshments at 3:30pm, MSB 4110)
LOCATION: Mathematical Sciences Building 1147

SPEAKER: Hua Tang, Dept of Genetics, Stanford University

TITLE: Learning Genetic Architecture of Complex Traits Across Populations


Genome-wide association studies (GWAS) have successfully revealed many loci that influence complex traits and disease susceptibilities. An unanswered question is “to what extent does the genetic architecture underlying a trait overlap between human populations?” We explore this question using blood lipid concentrations as a model trait. In African Americans and Hispanic Americans participating in the Women’s Health Initiative SNP Health Association Resource, we validated one African-specific HDL locus as well as 14 known lipid loci that have been previously implicated in studies of European populations. Moreover, we demonstrate striking similarities in genetic architecture (loci influencing the trait, direction and magnitude of genetic effects, and proportions of phenotypic variation explained) of lipid traits across populations. In particular, we found that a disproportionate fraction of lipid variation in African Americans and Hispanic Americans can be attributed to genomic loci exhibiting statistical evidence of association in Europeans, even though the precise genes and variants remain unknown. At the same time, we found substantial allelic heterogeneity within shared loci, characterized both by population-specific rare variants and variants shared among multiple populations that occur at disparate frequencies. The allelic heterogeneity emphasizes the importance of including diverse populations in future genetic association studies of complex traits such as lipids; furthermore, the overlap in lipid loci across populations of diverse ancestral origin argues that additional knowledge can be gleaned from multiple populations. We discuss how the overlapping genetic architecture can be exploited to improve the efficiency of GWAS in minority populations.

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