Abstract
It is believed that almost all common diseases are the consequence of complex interactions between genetic markers and environmental factors. However, few such interactions have been documented to date. Conventional statistical methods for detecting gene and environmental interactions are often based on the linear regression model, which assumes a linear interaction effect. In this study, we propose a nonparametric partition-based approach that is able to capture complex interaction patterns. We apply this method to the real data set of hypertension provided by Genetic Analysis Workshop 18. Compared with the linear regression model, the proposed approach is able to identify many additional variants with significant gene-environmental interaction effects. We further investigate one single-nucleotide polymorphism identified by our method and show that its gene-environmental interaction effect is, indeed, nonlinear. To adjust for the family dependence of phenotypes, we apply different permutation strategies and investigate their effects on the outcomes.
Highlights
Genome-wide association studies (GWAS) have successfully discovered many common variants associated with complex diseases, but the single-nucleotide polymorphisms (SNPs) identified so far account for a small proportion of the total heritability in quantitative traits [1]
We propose a nonparametric partition-based approach to detect G×E interactions and conduct a GWAS for hypertension using the real data set provided by Genetic Analysis Workshop 18 (GAW18)
Partitions created by environmental factors The real data set from GAW18 contains the records of 4 environmental factors: age, gender, smoking status, and antihypertensive medication usage
Summary
Genome-wide association studies (GWAS) have successfully discovered many common variants associated with complex diseases, but the single-nucleotide polymorphisms (SNPs) identified so far account for a small proportion of the total heritability in quantitative traits [1]. If β3 = 0, the conditional effect of the SNP is constant across different levels of the environmental factor and we conclude that there is no G×E interaction. This model assumes a linear interaction effect; given G, the outcome y is linearly related with E. For each SNP, both the linear regression model and the proposed method are used to evaluate its interaction effect with each of the 4 environmental factors: age, gender, smoking status, and medicine. We note that, compared with the linear model, the proposed method is able to identify many additional SNPs. We further study the interaction pattern between SNP rs17206492 and medicine, and find that this interaction effect is, nonlinear. We investigate different permutation strategies in the presence or absence of pedigree dependence of the phenotype
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
