| Title: | Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data |
| Journal: | Behavior Genetics |
| Published: | 26 Apr 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33899139/ |
| DOI: | https://doi.org/10.1007/s10519-021-10058-8 |
| Citations: | 3 (2 in last 2 years) as of 8 Aug 2024 |
Publication 5961
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Abstract
Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.</p>
4 Authors
- Brad Verhulst
- Joshua N. Pritikin
- James Clifford
- Elizabeth Prom-Wormley
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