A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets
A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets
Abstract Current methods to evaluate gene-by-environment (GxE) interactions on biobank-scale datasets are limited. MonsterLM enables multiple linear regression on genome-wide datasets, does not rely on parameters specification and provides unbiased estimates of variance explained by GxE interaction effects. We applied MonsterLM to the UK Biobank for eight blood biomarkers (N=325,991), identifying significant genome-wide interaction variance with waist-to-hip ratio for five biomarkers, with variance explained by interactions ranging from 0.11 to 0.58. 48% to 94% of GxE interaction variance can be attributed to variants without significant marginal association with the phenotype of interest. Conversely, for most traits, >40% of interaction variance was explained by less than 5% of genetic variants. We observed significant improvements in polygenic score prediction with incorporation of GxE interactions in four biomarkers. Our results imply an important contribution of GxE interaction effects, driven largely by a restricted set of variants distinct from loci with strong marginal effects.
Khan Mohammad、Chong Michael、Petch Jeremy、Judge Conor、Di Shuang、Par¨| Guillaume、Mao Shihong、Di Scipio Matteo、Perrot Nicolas、Nelson Walter
Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University||Department of Medicine, Faculty of Health Sciences, McMaster UniversityPopulation Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster UniversityPopulation Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University||Department of Medicine, Faculty of Health Sciences, McMaster University||Centre for Data Science and Digital Health, Hamilton Health Sciences||Institute of Health Policy, Management and Evaluation, University of TorontoPopulation Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster UniversityCentre for Data Science and Digital Health, Hamilton Health Sciences||Dalla Lana School of Public Health, University of TorontoPopulation Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University||Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute||Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine||Department of Health Research Methods, Evidence, and Impact, McMaster UniversityPopulation Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster UniversityPopulation Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University||Department of Medicine, Faculty of Health Sciences, McMaster UniversityPopulation Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster UniversityCentre for Data Science and Digital Health, Hamilton Health Sciences
生物科学研究方法、生物科学研究技术基础医学遗传学
Gene-environment interactionsgenome-wide linear regressioncardiometabolic biomarkersinteraction polygenic scorewaist-hip-ratio
Khan Mohammad,Chong Michael,Petch Jeremy,Judge Conor,Di Shuang,Par¨| Guillaume,Mao Shihong,Di Scipio Matteo,Perrot Nicolas,Nelson Walter.A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets[EB/OL].(2025-03-28)[2025-08-07].https://www.medrxiv.org/content/10.1101/2021.04.24.21255884.点此复制
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