Abstract
Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.
Highlights
Common genetic variants, identified by genome-wide association studies (GWAS), generally explain only a very small proportion of the variance of complex diseases or traits [1,2]
We present a novel statistical method for gene–environment interactions (G×E) analysis, namely the Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E)
HisCoM-G×E can be extended to a statistical method that can take into account various distribution types by using the framework of generalized linear models (GLMs) applied in the PHARAOH method [18]
Summary
Common genetic variants, identified by genome-wide association studies (GWAS), generally explain only a very small proportion of the variance of complex diseases or traits [1,2]. This may be because the genetic risk of single nucleotide polymorphisms (SNPs) is modified by environment, gender, age, or other factors. Understanding those interaction effects can help us to explain the “missing heritability” problem of complex diseases or traits [2]. Several studies of gene–environment interactions (G×E) have been performed for a variety of human complex diseases [3,4,5]. An implementation of HisCoM-G×E can be downloaded from the website http://statgen.snu.ac.kr/software/hiscom-gxe
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