RT Journal Article
SR Electronic
T1 EigenGWAS: finding loci under selection through genome-wide association studies of eigenvectors in structured populations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 023457
DO 10.1101/023457
A1 Guo-Bo Chen
A1 Sang Hong Lee
A1 Zhi-Xiang Zhu
A1 Beben Benyamin
A1 Matthew R. Robinson
YR 2015
UL http://biorxiv.org/content/early/2015/07/29/023457.abstract
AB We apply the statistical framework for genome-wide association studies (GWAS) to eigenvector decomposition (EigenGWAS), which is commonly used in population genetics to characterise the structure of genetic data. We show that loci under selection can be detected in a structured population by using eigenvectors as phenotypes in a single-marker GWAS. We find LCT to be under selection between HapMap CEU-TSI cohorts, a finding that was replicated across European countries in the POPRES samples. HERC2 was also found to be differentiated between both the CEU-TSI cohort and among POPRES samples, reflecting the likely anthropological differences in skin and hair colour between northern and southern European populations. We show that when determining the effect of a SNP on an eigenvector, three methods of single-marker regression of eigenvectors, best linear unbiased prediction of eigenvectors, and singular value decomposition of SNP data are equivalent to each other. We also demonstrate that estimated SNP effects on eigenvectors from a reference panel can be used to predict eigenvectors (the projected eigenvectors) in a target sample with high accuracy, particularly for the primary eigenvectors. Under this GWAS framework, ancestry informative markers and loci under selection can be identified, and population structure can be captured and easily interpreted. We have developed freely available software to facilitate the application of the methods (https://github.com/gc5k/GEAR/wiki/EigenGWAS).