ArticleNature communications2023
A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Optimizing phenotype scale improves genetic analyses in large-scale biobanks.bioRxiv : the preprint server for biology · 2026Article
- The geometry of G × E: How scaling and endogenous treatment effects shape interaction direction.PLoS genetics · 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
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
- Moderating the Heritability of Body Mass Index by Age and Sex With Genomic Data.Genetic epidemiology · 2026Article
- Choice of phenotype scale is critical in biobank-based G×E tests.bioRxiv : the preprint server for biology · 2026Article
- Steatotic liver disease interacts with a polygenic risk score for triglyceride clearance to impact the risk of hypertriglyceridaemia: The Maastricht Study.Diabetologia · 2025Article
- Distinct explanations underlie gene-environment interactions in the UK Biobank.American journal of human genetics · 2025Article
- A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits.American journal of human genetics · 2024Article
- What Causes Premature Coronary Artery Disease?Current atherosclerosis reports · 2024Review
- LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.Briefings in bioinformatics · 2024Article
- Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding.Environmental science & technology · 2024Review
- Distinct explanations underlie gene-environment interactions in the UK Biobank.medRxiv : the preprint server for health sciences · 2024Article
- A method to estimate the contribution of rare coding variants to complex trait heritability.Nature communications · 2024Article
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13 authors.
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Abstract
Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.
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