Set based tests for genetic association and gene-environment interaction in longitudinal studies

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Most human diseases have a multifactorial etiology, characterizing complex interplay of multiple genes and environmental factors. The effects of these genetic and environmental factors on disease risk are likely to change dynamically over different life stages. Longitudinal studies of risk factors for common and chronic diseases like blood pressure, body mass index, provide a valuable opportunity to explore how genetic variants affect these traits over time. The ability to detect disease susceptibility genes can be improved if we jointly utilize the entire set of longitudinal outcomes. Moreover, since disease risk factors and phenotypes are likely influenced by the joint effect of multiple variants in a gene or in a genomic region, a joint analysis of these variants considering linkage disequilibrium and potential interactions among the variants may help to explain additional heritability. Integrating repeated measures of environmental exposure data into these genetic association models will help to identify specific sub-groups of individuals who may be more susceptible to environmental exposures. Identification of gene-environment interactions may have implications for targeted intervention and prevention. In this project, the investigators will try to utilize the temporally varying outcome-exposure profile available in a longitudinal genetic association study to enhance the power of statistical tests for genetic association and interaction. Using data from a muti-ethnic cohort, the project team will explore time-dependent genetic associations and gene-environment interactions with genes and pathways as the unit of analysis instead of a single marker at a given locus. This approach is biologically more meaningful as genes are the functional units, not the single nucleotide polymorphisms and by joint analysis of rare and common genetic variants in a region one may be closer to capturing functional variation. Several environmental factors measuring an individual's diet, physical activity, psychosocial behavior and perception of the neighborhood they live in will be considered in the planned analysis. There are several technical challenges that will be addressed in the project. A primary goal of the study team will be to develop simple generalized score tests derived under a random field model involving multiple phenotypes, genes and environmental factors in a longitudinal study. The approach reduces the dimensionality of the inference problem by translating the association testing involving many predictors in terms of a reduced number of parameters and resultant tests with reduced degrees of freedom. The developed methods will use and extend classical spatial random field theory and recent results on multi-marker tests to characterize complex time-dependent associations and interactions. Several essential methodological improvements necessary for handling longitudinal data will be carried out to enhance the robustness to misspecification of within subject correlation structure and to improve computational efficiency. The methods will then be extended to a gene-environment set association test using longitudinal data. Several important dimension reduction techniques to handle correlated environmental exposure data are proposed. The project team also considers treatment of time varying exposure and time varying interaction effects under this set-based framework. There are no multi-marker based tests presently available in the literature that use the richness of longitudinal outcome and exposure data and the current project is expected to fill that gap. To summarize, the project introduces a novel genetic random field framework to formulate this class of multivariable association problems involving disease outcomes, gene, environment, and time to lead to powerful statistical inference.