Joint genetic analysis using variant sets reveals polygenic gene-context interactions

Joint genetic models for multiple traits have helped to enhance association analyses. most existing multi-trait models have been designed to increase power for detecting associations,whereas the analysis of interactions has received considerably less attention. here,we propose iset,a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. we first use simulations to validate iset before applying the model to the analysis of genotype-environment interactions in an eqtl study. our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. finally,we apply iset to test for sub-group specific genetic effects in human lipid levels in a large human cohort,where we identify a gene-sex interaction for c-reactive protein that is missed by alternative methods. © 2017 casale et al.