A partition-based approach to identify gene-environment interactions in genome wide association studies.

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-n

SUBMITTER: Fan R 

PROVIDER: S-EPMC4143762 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature