Project Summary/AbstractThe etiology of many complex human diseases/disorders is multi-factorial involving the contribution of genetics,environmental exposures as well as complicated interactions between them. As living organisms, people areexposed to multiple environmental risk factors, such as chemical contaminants and non-chemical stressors(e.g., nutrients intake, hormone level and stress) on a daily basis. There is clear evidence that disease risk canbe modified by simultaneous and sequential exposure to multiple environmental factors, larger than whatwould be expected from simple addition of the effects of the factors acting alone. Thus, the ?singleenvironmental exposure? approach cannot capture the combined environmental effect and their synergisticinteractions with our genetic system. Built upon our previous methodology development on GﾗE interactions,the long-term goal of the research is to understand and gain novel insights into how environmental mixturesjointly moderate genetic influences on disease risk with longitudinal genetic data. Our objective is to developpowerful statistical methods to understand how genes interact with multiple environmental exposures as awhole to affect disease risk and to further dissect the dynamics of GﾗE effects. Specifically, we try to address:1) Which genetic variants are sensitive to multiple environmental exposures to affect disease risk? 2) Whichmixtures of environmental exposures are responsible for the risk? and 3) What is the dynamics of thesynergistic GﾗE effects over time? Non- and semi-parametric methods will be developed to model and testsynergistic GﾗE effect with longitudinal data. We will apply the methods to a longitudinal study of GﾗEinteractions on eating disorder (ED) and explore the mechanism of gene by hormone interaction on woman?seating behavior. We will provide efficient estimation and testing procedures with asymptotic evaluations. Userfriendly computational tools will be made available for public use. The success will provide important tools tofacilitate the process of disease gene identification, and advance the discovery of novel genes andenvironmental risk factors to facilitate identification of drug targets and better prevention strategies to enhancepublic health. In addition, novel genetic and environmental interaction findings based on the ED GWAS datawill likely provide new insights into the etiology of eating disorder in women.