Computational Statistic Approaches to Gene-Environment Interaction

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DESCRIPTION (provided by applicant): Complex disorders such as depression, type 2 diabetes and asthma are caused jointly by genetic and environmental risk factors. Understanding these risk factors will improve diagnosis and treatment for many such disorders. Recent results suggest that genes and environment often interact in a nonlinear manner; genetic risk variants may increase the vulnerability to or adverse consequences of exposure, but have no direct effect on disease risk on their own. Under this model, including environmental covariates can increase the power of a genome scan considerably. However, studies of genetic association have been hampered by the lack of methods to assess gene-environment interaction. Due to the large number of hypotheses possible, we feel that detailed definitions of phenotypes and precise modeling of genetic architecture are required to design powerful studies. We propose to develop statistical methods to estimate gene-environment interaction both from family data and from samples of unrelated individuals in a genome-wide association (GWA) study. To this end, we have assembled an interactive and innovative team with a proven track record in the development of methods for the analysis of gene-mapping data. Our approach is based on mapping risk variants for common complex disorders by combining information of multiple tightly linked markers and environmental covariates. Furthermore we propose algorithms and simulation tools to estimate the strength of gene-environment interaction and to plan replication studies. All the tools and methods we develop will be incorporated into publicly available software. We have access to genotype and phenotype data from the NIMH bipolar genetics initiative. We intend to use this dataset as well as simulated datasets and other GWA datasets to evaluate and calibrate our methods for estimating genotype-phenotype interaction and for planning replication studies. (End of Abstract)