ArticleGenetic epidemiology2020
Efficient gene-environment interaction tests for large biobank-scale sequencing studies.
Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores.Molecular neurodegeneration · 2023Pooled it
- Article
- Metabolomic Signatures of Prediabetes in Mexican Americans: The Role of Genetics and Macronutrients.Advanced genetics (Hoboken, N.J.) · 2025Article
- Alzheimer's disease multi-ancestry genome-wide interaction and stratified study with smoking.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Robust Mixed Model Association Test for Gene-Environment Interactions.medRxiv : the preprint server for health sciences · 2025Article
- Detection of genomic regions affecting thermotolerance traits in growing pigs during acute and chronic heat stress.Genetics, selection, evolution : GSE · 2025Article
- Genome-wide pleiotropy analysis of longitudinal blood pressure and harmonized cognitive performance measures.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Efficient and accurate framework for genome-wide gene-environment interaction analysis in large-scale biobanks.Nature communications · 2025Article
- Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.G3 (Bethesda, Md.) · 2025Article
- Analyzing longitudinal trait trajectories using GWAS identifies genetic variants for kidney function decline.Nature communications · 2024Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Gene-Excessive Sleepiness Interactions Suggest Treatment Targets for Obstructive Sleep Apnea Subtype.medRxiv : the preprint server for health sciences · 2024Article
- Helicobacter pylori Treatment and Gastric Cancer Risk Among Individuals With High Genetic Risk for Gastric Cancer.JAMA network open · 2024Article
- A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects.Behavior genetics · 2023Article
- Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation.Current nutrition reports · 2022Review
- A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.PLoS genetics · 2022Article
- Gene-Environment Interactions for Cardiovascular Disease.Current atherosclerosis reports · 2021Review
- Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data.Frontiers in genetics · 2021Review
- SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data.Frontiers in genetics · 2021Article
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9 authors.
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Abstract
Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.