Date of Award

Fall 1-1-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Public Health

First Advisor

Zhang, Heping

Abstract

Psychiatric disorders are among the most common diseases of humans, affecting a large number of individuals worldwide and contributing to a considerable burden of disease and disability. With a lifetime prevalence ranging from 5% to 17%, depression – characterized by a persistent low mood or loss of interest in activities for extended periods – is prominent amongst psychiatric disorders. Despite the availability of new medications developed over the past decade to treat depression, long-term treatment response remains disappointing; the risk of death by suicide is over 8-times national averages. Depression is multifactorial with both genetic and environmental factors, including demographic variables like sex and race/ethnicity and exposures such as trauma and socioeconomic stress. Emerging evidence highlights the importance of gene-environment (G×E) interactions in shaping individual risk. Understanding these interactions is crucial for uncovering the underlying biological mechanisms of depression and improving strategies for treatment. Despite growing interest in G×E interactions, several key challenges have hindered progress in the field. These include lack of population diversity, a narrow focus on environmental exposures, limited statistical power due to high-dimensional data and multiple testing, and restrictive modeling assumptions. This dissertation addresses these limitations by developing novel methods to detect complex G×E interactions in large and diverse datasets. The overarching goal is to deepen our understanding of how genetic and environmental factors jointly contribute to depression risk. In Chapter 2, we explore genetic interactions by sex and race/ethnicity in depression using data from the diverse All of Us cohort. The study identifies genetic variants linked to depression and shows a sex-specific association at the ESR1 gene that replicates in the UK Biobank and aligns with prior research. These findings emphasize the value of diverse populations and the importance of examining genetic interactions across demographic groups. In Chapter 3, we introduce a flexible forest-based method for detecting gene-environment interactions that overcomes common limitations of existing approaches, such as limited power and restrict model assumptions. The method performs well in simulations and identifies interactions between genetic variation and childhood trauma related to depression risk in the UK Biobank. Key findings are replicated in the external dataset and supported by prior literature, demonstrating the methods robustness and utility in large-scale studies. In Chapter 4, we enhance our forest-based method by incorporating set-based aggregation strategies to improve power in detecting G×E interactions. By grouping SNPs into gene sets using polygenic risk scores or super-variants, we identify significant gene–trauma interactions linked to depression in the UK Biobank. These results highlight the powerfulness of our approach and support the polygenic nature of depression. In summary, this dissertation presents powerful statistical methods and empirical analyses to advance the understanding of G×E interactions in depression. By demonstrating how genetic risk is shaped by environmental exposures, this work not only enhances our understanding of depression but also provides broadly applicable tools for studying complex diseases including psychiatric disorders, with implications for improving prevention, diagnosis, and treatment strategies.

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