EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations.

ABSTRACT: We develop a novel approach to identify regions of the genome underlying population genetic differentiation in any genetic data where the underlying population structure is unknown, or where the interest is assessing divergence along a gradient. By combining the statistical framework for genome-wide association studies (GWASs) with eigenvector decomposition (EigenGWAS), which is commonly used in population genetics to characterize the structure of genetic data, loci under selection can be identified without a requirement for discrete populations. We show through theory and simulation that our approach can identify regions under selection along gradients of ancestry, and in real data we confirm this by demonstrating LCT to be under selection between HapMap CEU-TSI cohorts, and we then valid

SUBMITTER: Chen GB 

PROVIDER: S-EPMC4901357 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature