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Abstract

Decoding the etiology of complex human diseases is one of the central topics in biomedical research. This dissertation investigates how genetic and environmental factors contribute to disease incidence by leveraging the power of computational models and large-scale observational data. Chapter 1 briefly overviews the classical assumptions, models, and methods underlying the problem. We examine how we might build computational models to infer the genetic and environmental effects on disease etiology based on the observable outcome. Chapter 2 introduces a paradigm that studies the health effects of a short-term exterior intervention: the daylight-saving time shift that changes time forward and backward by one hour in spring and autumn. In Chapter 3, we expand our subject to long-term environmental factors and discuss using a Bayesian model to probe psychiatric disease seasonality and trends. In Chapter 4, we consider an even more complicated problem and explore how we might construct models to jointly infer the effects of various environmental qualities, geographic locations, genetic factors, and gene-environmental interactions. The study shows explicitly that varying environmental factors can sway the genetic influence on disease etiology (the heterogeneity of genetic effects). Lastly, Chapter 5 sums up our studies and concludes our computational dissection of complex human disease etiology. We discuss what additional knowledge we have contributed towards understanding the cause and complex disease development. Also, in the last chapter, we present some directions for future exploration.

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