4.3 Article

Smooth-threshold multivariate genetic prediction incorporating gene-environment interactions

期刊

G3-GENES GENOMES GENETICS
卷 11, 期 12, 页码 -

出版社

OXFORD UNIV PRESS INC
DOI: 10.1093/g3journal/jkab278

关键词

genetic prediction; gene-environment interaction; smooth thresholding

资金

  1. JSPS KAKENHI [20K11723, 20H00576]
  2. Grants-in-Aid for Scientific Research [20H00576, 20K11723] Funding Source: KAKEN

向作者/读者索取更多资源

The proposed genetic prediction modeling approach extends previous methods to include gene-environment interaction effects, demonstrating superior performance compared to other modeling approaches through simulation studies and real data analysis. The model's ability to simultaneously consider multiple environmental variables and automatically remove irrelevant predictors makes it a valuable tool for GWAS studies.
We propose a genetic prediction modeling approach for genome-wide association study (GWAS) data that can include not only marginal gene effects but also gene-environment (GxE) interaction effects-i.e., multiplicative effects of environmental factors with genes rather than merely additive effects of each. The proposed approach is a straightforward extension of our previous multiple regression-based method, STMGP (smooth-threshold multivariate genetic prediction), with the new feature being that genome-wide test statistics from a GxE interaction analysis are used to weight the corresponding variants. We develop a simple univariate regression approximation to the GxE interaction effect that allows a direct fit of the STMGP framework without modification. The sparse nature of our model automatically removes irrelevant predictors (including variants and GxE combinations), and the model is able to simultaneously incorporate multiple environmental variables. Simulation studies to evaluate the proposed method in comparison with other modeling approaches demonstrate its superior performance under the presence of GxE interaction effects. We illustrate the usefulness of our prediction model through application to real GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

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