ArticleBioinformatics (Oxford, England)2021
GEM: scalable and flexible gene-environment interaction analysis in millions of samples.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 5 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants.Atherosclerosis · 2026Pooled it
- Exploiting meta-analysis of genome-wide interaction with serum 25-hydroxyvitamin D to identify novel genetic loci associated with pulmonary function.The American journal of clinical nutrition · 2024Pooled it
- Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS and impact on esophageal cancer risk in Japanese people.Science advances · 2024Pooled it
- Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.Bioinformatics (Oxford, England) · 2023Pooled it
- Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.Nature communications · 2022Pooled it
- Genome-wide association study of sarcopenia index reveals sex-stratified genetic architecture.Biology of sex differences · 2026Article
- Genomic interplay between deployment exposures and Gulf War illness in Million Veteran Program participants.Human genomics · 2026Article
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
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- Genome-wide gene-environment interaction study uncovers 162 vitamin D status variants using a precise ambient UVB measure.Nature communications · 2025Article
- Robust Mixed Model Association Test for Gene-Environment Interactions.medRxiv : the preprint server for health sciences · 2025Article
- Identification of gene-sex hormone interactions associated with type 2 diabetes among men and women.PLoS genetics · 2025Article
- A large-scale genome-wide study of gene-sleep duration interactions for blood pressure in 811,405 individuals from diverse populations.Molecular psychiatry · 2025Article
- A multi-level gene-diet interaction analysis of fish oil and 14 polyunsaturated fatty acid traits identifies the FADS and GPR12 loci.HGG advances · 2025Article
- Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors.GeroScience · 2025Article
- Pathway-specific polygenic scores substantially increase the discovery of gene-adiposity interactions impacting liver biomarkers.medRxiv : the preprint server for health sciences · 2025Article
- Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks.Nature communications · 2025Article
- Decomposed interaction testing improves detection of genetic modifiers of the relationship of dietary omega-3 fatty acid intake and its plasma biomarkers with hsCRP in the UK Biobank.Genes & nutrition · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
motivationGene-environment interaction (GEI) 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.
resultsHere, 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. AVAILABILITY AND IMPLEMENTATION: GEM is freely available as an open source project at https://github.com/large-scale-gxe-methods/GEM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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