Abstract
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.
Highlights
Understanding genetic interactions with external context (GxC), including environment, is a major challenge in quantitative genetics
We derive a method based on linear mixed models that can be applied to both of these designs. interaction set test (iSet) enables testing for interactions between context and sets of variants, and accounts for polygenic effects
We find that modeling interactions with variant sets offers increased power, thereby uncovering interactions that cannot be detected by alternative methods
Summary
Understanding genetic interactions with external context (GxC), including environment, is a major challenge in quantitative genetics. Multivariate formulations of LMMs have been developed to test for genetic effects across multiple correlated traits, both in single-variant analyses [10, 11] and more recently for joint tests using variant sets [12]. These existing multivariate LMMs have primarily been designed to increase the statistical power for detecting association signals, whereas methods to test for interactions are only beginning to emerge [10, 13]. We here show that joint tests across multiple contexts and sets of variants allow for characterizing the local architecture of polygenic-GxC interactions
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