Decoding Complex Trait Genetics by Linking Variants to Regulatory Landscapes

Understanding how genetic variants influence complex traits and diseases is a central challenge in human genetics. Genome-wide association studies (GWAS) have identified thousands of loci associated with various traits, yet the majority reside in non-coding regions, and their functional mechanisms remain unclear. Current approaches primarily focus on cis regulatory effects and gene expression, which account for only a fraction of trait heritability. This gap underscores the need to explore additional regulatory mechanisms, including trans gene regulation and chromatin modifications, to provide a more comprehensive understanding of complex trait genetics. This dissertation addresses two critical questions: (1) How can we map trans regulatory effects of genetic variants on genes and networks (trans-eQTL) with greater power and accuracy? (2) How can we enhance the detection of chromatin QTLs (cQTL) to better understand their role in gene regulation and complex traits? To answer these questions, I developed two novel statistical methods: trans-PCO for mapping trans-eQTLs and CACTI for identifying cQTLs. Both methods leverage multivariate association testing to improve statistical power by combining correlated features, such as co-regulated genes or nearby regulatory elements, into joint analyses. In Chapter 2, trans-PCO identified high-quality trans-eQTLs that link trait associated variants to gene networks and biological pathways, providing new insights into the role of trans regulatory networks in complex traits. In Chapter 3, CACTI improved cQTL mapping by leveraging correlations among neighboring regulatory elements, enabling the construction of a comprehensive map of cQTLs across diverse histone marks, tissues, and cell types, and emphasizing the importance of studying molecular traits beyond gene expression. This dissertation provides valuable methodologies and resources for the field by bridging gaps in trans-eQTL and cQTL mapping. These findings advance our understanding of regulatory mechanisms underlying complex traits and open up opportunities for integrative studies that link genetic variants to multi-layered regulatory mechanisms and complex traits and diseases.