Powerful Multilocus Tests of Genetic Association in the Presence of Gene-Gene and Gene-Environment Interactions

Abstract

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.

Gene-based interaction analysis by incorporating external linkage disequilibrium information

“…We note that there exist gene-based methods that jointly test for the main effect and the interaction effect. 23,24 Although the goals of these tests are slightly different from ours, they all aim to incorporate information contributed by multiple markers in a gene. How to extend the proposed PC framework to jointly test for the main and interaction effects would merit further research.…”

Bayesian Genetic Association Test when Secondary Phenotypes Are Available Only in the Case Group

“…In addition, the effects of genes (SNPs) and covariates on the selected disease trait are often described through a multivariate linear or generalized linear model. Through a set of latent biological phenotypes, Chatterjee et al [5] described a conceptual framework for modeling genetic associations and gene-gene and gene-environment interactions in indirect-association studies with multivariate logistic regression models. Maity et al [6] extended the approaches proposed by Chatterjee et al [5] to studies with repeated measures data and developed a class of score tests in general semi-parametric regression models.…”

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

“…They are the third generation genetic markers in humans and play an important role in identifying disease-related genes, elucidating phenotypic differences among individuals, and interpreting disease susceptibilities in different populations and individuals. Previous studies have shown that the genesis and development of complicated diseases are not completely caused by genetic factors; rather, they are results of the interactions between genetic variations and environmental factors (Chatterjee et al, 2006;Wong et al, 2010). It is likely that there is only weak relevance, but not a major genetic effect between every individual gene and disease.…”