Quantification of the overall contribution of gene-environment interaction for obesity-related traits.
- Sulc J University Center for Primary Care and Public Health, University of Lausanne, Lausanne, 1010, Switzerland.
- Mounier N University Center for Primary Care and Public Health, University of Lausanne, Lausanne, 1010, Switzerland.
- Günther F Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
- Winkler T Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
- Wood AR Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK.
- Frayling TM Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK.
- Heid IM Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
- Robinson MR Department of Computational Biology, University of Lausanne, Lausanne, 1015, Switzerland.
- Kutalik Z University Center for Primary Care and Public Health, University of Lausanne, Lausanne, 1010, Switzerland. zoltan.kutalik@unil.ch.
- 2020-03-15
Published in:
- Nature communications. - 2020
Body Mass Index
Databases, Factual
Gene-Environment Interaction
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Models, Genetic
Obesity
Phenotype
Polymorphism, Single Nucleotide
Risk Factors
United Kingdom
English
The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass).
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1038/s41467-020-15107-0
- PMID 32170055
- Persistent URL
- https://sonar.ch/global/documents/278168
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