A fast linkage disequilibrium-based statistical test for Genome-Wide Epistatic Selection Scans in structured populations

Abstract

AbstractThe quest for genome-wide signatures of selection in populations using SNP data has proven efficient to uncover genes involved in conserved or adaptive molecular functions, but none of the statistical methods were designed to identify interacting genes as targets of selective processes. Here, we propose a straightforward statistical test aimed at detecting epistatic selection, based on a linkage disequilibrium (LD) measure accounting for population structure and heterogeneous relatedness between individuals. SNP-based (Trv) and window-based (TcorPC1v) statistics fit a Student distribution, allowing to easily and quickly test the significance of correlation coefficients in the frame of Genome-Wide Epistatic Selection Scans (GWESS) using candidate genes as baits. As a proof of concept, use of SNP data from theMedicago truncatulasymbiotic legume plant uncovered a previously unknown gene coadaptation between theMtSUNN(Super Numeric Nodule) receptor and theMtCLE02(CLAVATA3-Like) signalling peptide, and experimental evidence accordingly supported aMtSUNN-dependent negative role ofMtCLE02in symbiotic root nodulation. Using human HGDP-CEPH SNP data, our new statistical test uncovered strong LD betweenSLC24A5andEDARworldwide, which persists after correction for population structure and relatedness in Central South Asian populations. This result suggests adaptive genetic interaction or coselection between skin pigmentation and the ectodysplasin pathway involved in the development of ectodermal organs (hairs, teeth, sweat glands), in some human populations. Applying this approach to genome-wide SNP data will foster the identification of evolutionary coadapted gene networks.Author summaryPopulation genomic methods have allowed to identify many genes associated with adaptive processes in populations with complex histories. However, they are not designed to identify gene coadaptation between genes through epistatic selection, in structured populations. To tackle this problem, we developed a straightforward LD-based statistical test accounting for population structure and heterogeneous relatedness between individuals, using SNP-based (Trv) or windows-based (TcorPC1v) statistics. This allows easily and quickly testing for significance of correlation coefficients between polymorphic loci in the frame of Genome Wide Epistatic Selection Scans (GWESS). Following detection of gene coadaptation using SNP data from human and the model plantMedicago truncatula, we report experimental evidence of genetic interaction between two receptors involved in the regulation of root nodule symbiosis inMedicago truncatula. This test opens new avenues for exploring the evolution of genes as interacting units and thus paves the way to infer new networks based on evolutionary coadaptation between genes.