| Title: | A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets |
| Journal: | Nature Communications |
| Published: | 25 Aug 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37626057/ |
| DOI: | https://doi.org/10.1038/s41467-023-40913-7 |
| URL: | https://www.nature.com/articles/s41467-023-40913-7.pdf |
| Citations: | 9 (9 in last 2 years) as of 8 Aug 2024 |
Publication 9706
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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.</p>
13 Authors
- 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
- Guillaume Paré
1 Application
| Application ID | Title |
|---|---|
| 15255 | Identification of the shared biological and sociodemographic factors underlying cardiovascular disease and dementia risk |
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