Open AccessJoint genetic analysis using variant sets reveals polygenic gene-context interactions
Author(s) -
Francesco Paolo Casale,
Danilo Horta,
Barbara Rakitsch,
Oliver Stegle
Publication year - 2017
Publication title -
plos genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.587
H-Index - 233
eISSN - 1553-7404
pISSN - 1553-7390
DOI - 10.1371/journal.pgen.1006693
Subject(s) - biology , genetic architecture , trait , context (archaeology) , computational biology , expression quantitative trait loci , genetics , linear model , genetic association , gene–environment interaction , quantitative trait locus , gene , evolutionary biology , genotype , machine learning , computer science , single nucleotide polymorphism , paleontology , programming language
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.