ArticleAmerican journal of human genetics2020
Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model.
Article in American journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.Bioinformatics (Oxford, England) · 2023Pooled it
- Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods.Nature communications · 2022Trial
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- From QTL to candidate genes: a data-driven approach to unravel the genetic architecture of yellow rust resistance in central European wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- A fast method for breeding by design via G × E interactions detected in large-scale climatic, phenomic and genomic data.National science review · 2026Article
- A biobank-scale method for learning modulators of gene-environment interaction underlying human complex traits from multiple environmental exposures.bioRxiv : the preprint server for biology · 2026Article
- When does accounting for gene-environment interactions improve complex trait prediction? A case study with Drosophila lifespan.G3 (Bethesda, Md.) · 2026Article
- Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026Article
- Hidden structure in polygenic scores and the challenge of disentangling ancestry interactions in admixed populations.Genetics · 2025Article
- Studying rare variant polygenic risk scores using whole exome sequencing and imputed genotype data.Communications biology · 2025Article
- Robust Mixed Model Association Test for Gene-Environment Interactions.medRxiv : the preprint server for health sciences · 2025Article
- Incorporating additive genetic effects and linkage disequilibrium information to discover gene-environment interactions using BV-LDER-GE.Genome biology · 2025Article
- PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits.Nature human behaviour · 2025Article
- When does accounting for gene-environment interactions improve complex trait prediction? A case study withbioRxiv : the preprint server for biology · 2025Article
- Improvement of polygenic modeling of blood pressure traits using lifestyle information in the UK Biobank.Genetics · 2025Article
- Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks.Nature communications · 2025Article
- The integration of quantile regression with 3VmrMLM identifies more QTNs and QTN-by-environment interactions using SNP- and haplotype-based markers.Plant communications · 2025Article
- Distinct explanations underlie gene-environment interactions in the UK Biobank.American journal of human genetics · 2025Article
- High-Dimensional Gene-Environment Interaction Analysis.Annual review of statistics and its application · 2025Article
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2 authors.
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Abstract
The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that 9.3%, 3.9%, 1.6%, and 12.5%, respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores (-log
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