A rapid method for the identification of epistatic 'dormant' SNPs

Abstract

We present a unique computational approach for the identification of epistatic SNPs based on SNPs with significant yet opposed effects depending on the genetic background. We introduce the mechanical heuristics of the approach based on first, binning the population according to their genomic-estimated breeding value (GEBV) and second, performing genome-wide association studies (GWAS) within each bin. SNPs are deemed to be epistatic if significant but with different signed effects in the GWAS from the most extreme bins containing individuals with the lowest and highest GEBV. We then show that these heuristics are equivalent to a regression of residuals on GEBV. Next, we illustrate our approach with a dataset of 2,111 cattle genotyped for 651,253 SNPs and using yearling weight as the phenotype. We identify 243 epistatic SNPs, and argue that these SNPs are 'dormant' with an additive effect waiting to be 'released' if selection moves the population to either tail of the genetic value distribution.