We found widespread evidence that gene-gene interactions influence traits, and that accounting for interactions identifies loci not previously identified in traditional single-locus association tests, because the interactions mask the main effects when tested in isolation. We applied the method to 12 complex traits in humans across four tissues or cross-tissue expression measures. We developed a new method to comprehensively test associations of all pairwise gene-gene interactions with complex traits using imputed expression. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. It remains unknown to what extent gene-gene interactions contribute to complex traits.
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