title: 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-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.
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, 10th Floor, New York, NY 10027, USA.
authors.forename: Ruixue
authors.initials: R
authors.lastname: Fan
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, 10th Floor, New York, NY 10027, USA.
authors.forename: Chien-Hsun
authors.initials: CH
authors.lastname: Huang
authors.affiliation: ISOM, Hong Kong University of Science and Technology, Hong Kong.
authors.forename: Inchi
authors.initials: I
authors.lastname: Hu
authors.affiliation: Division of Biostatistics, School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong.
authors.forename: Haitian
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, 10th Floor, New York, NY 10027, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Zheng
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, 10th Floor, New York, NY 10027, USA.
authors.forename: Shaw-Hwa
authors.initials: SH
authors.lastname: Lo