GEM: scalable and flexible gene-environment interaction analysis in millions of samples

Motivation: Gene-environment interaction (GED studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases and traits. However, commonly used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples. Results: Here, we develop a new software program, GEM (Gene-Environment interaction analysis in Millions of samples), which supports the inclusion of multiple GEI terms, adjustment for GEI covariates and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic main and interaction effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352 768 unrelated individuals from the UK Biobank, identifying 24 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of the genetic architecture of complex diseases and traits.

Automated summary

Gene-environment interaction studies provide insights into how genetic variants modify the effects of environmental, physiological, lifestyle or treatment factors on complex traits. The newly developed software program GEM allows for robust analysis of multiple GEI terms and adjustment for covariates, with the capability to efficiently handle large sample sizes. This software enables large-scale GEI studies and has the potential to advance our understanding of the genetic architecture of complex diseases and traits.