A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets
© 2023. Springer Nature Limited..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Nature communications - 14(2023), 1 vom: 25. Aug., Seite 5196 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Di Scipio, Matteo [VerfasserIn] |
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| Anmerkungen: |
Date Completed 28.08.2023 Date Revised 19.11.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41467-023-40913-7 |
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| 245 | 1 | 2 | |a A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets |
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| 520 | |a © 2023. Springer Nature Limited. | ||
| 520 | |a 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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| 700 | 1 | |a Khan, Mohammad |e verfasserin |4 aut | |
| 700 | 1 | |a Mao, Shihong |e verfasserin |4 aut | |
| 700 | 1 | |a Chong, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Judge, Conor |e verfasserin |4 aut | |
| 700 | 1 | |a Pathan, Nazia |e verfasserin |4 aut | |
| 700 | 1 | |a Perrot, Nicolas |e verfasserin |4 aut | |
| 700 | 1 | |a Nelson, Walter |e verfasserin |4 aut | |
| 700 | 1 | |a Lali, Ricky |e verfasserin |4 aut | |
| 700 | 1 | |a Di, Shuang |e verfasserin |4 aut | |
| 700 | 1 | |a Morton, Robert |e verfasserin |4 aut | |
| 700 | 1 | |a Petch, Jeremy |e verfasserin |4 aut | |
| 700 | 1 | |a Paré, Guillaume |e verfasserin |4 aut | |
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