A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Matteo Di Scipio, Mohammad Khan, Shihong Mao, Michael Chong, Conor Judge, Nazia Pathan, Nicolas Perrot, Walter Nelson, Ricky Lali, Shuang Di, Robert Morton, Jeremy Petch and Guillaume Paré ()
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Matteo Di Scipio: Hamilton Health Sciences and McMaster University
Mohammad Khan: Hamilton Health Sciences and McMaster University
Shihong Mao: Hamilton Health Sciences and McMaster University
Michael Chong: Hamilton Health Sciences and McMaster University
Conor Judge: Hamilton Health Sciences and McMaster University
Nazia Pathan: Hamilton Health Sciences and McMaster University
Nicolas Perrot: Hamilton Health Sciences and McMaster University
Walter Nelson: Hamilton Health Sciences
Ricky Lali: Hamilton Health Sciences and McMaster University
Shuang Di: Hamilton Health Sciences
Robert Morton: Hamilton Health Sciences and McMaster University
Jeremy Petch: Hamilton Health Sciences and McMaster University
Guillaume Paré: Hamilton Health Sciences and McMaster University

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40913-7

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