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Integrative Approaches Toward Understanding 3D Genome Organization and Functional Impact of Human Genetic Variation
Li, Chong
Li, Chong
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2025-08
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Computer and Information Science
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https://doi.org/10.34944/rb3y-ng72
Abstract
Structural variants (SVs) are a major source of genetic variation and have been increasingly recognized for their potential to affect gene regulation and contribute to disease risk. One key way SVs exert their functional impact is by disrupting the three-dimensional (3D) genome architecture, a higher-order spatial organization in the nucleus. Such disruptions can lead to alterations in gene expression and disease phenotypes. However, the functional consequences of SVs on chromatin organization and their downstream regulatory effects remain poorly characterized. Existing approaches face several limitations. First, high-resolution datasets remain challenging to obtain, limiting the ability to detect fine-scale chromatin features. Second, the lack of integrative multi-omics analyses makes it insufficient to capture the broad regulatory landscape influenced by SVs. Furthermore, existing computational frameworks are not yet capable of efficiently and reliably characterizing the 3D genome structure from low-resolution Hi-C data. To address these limitations, this dissertation systematically investigates the impact of SVs on 3D genome organization and gene regulation through three complementary approaches: 1) an integrative bioinformatics pipeline was developed to construct an in-depth map of the 3D genome organization using chromosome conformation capture sequencing (Hi-C) data. Hi-C is a widely employed technology to characterize the structure of the genome and uncover folding principles of chromatin, such as A/B compartments, topologically associating domains (TADs), and loops. 2) a multi-omics analysis framework was implemented to assess the functional impact of SVs on chromatin conformation and gene regulation. By linking SVs to alterations in TAD boundaries, this study identified TAD-SVs that significantly altered chromatin architecture and gene regulation. By leveraging genome-wide association studies (GWAS) and quantitative trait loci (QTL) analyses, a set of SV- associated QTLs were characterized as potential drivers of gene expression change linked to complex traits. Additionally, SV-induced regulatory alterations on the Y chromosome were characterized by integrating Hi-C, DNA methylation, and RNA sequencing data; 3) a novel Generative Artificial Intelligence (AI) model (TRUHiC) was introduced to address the challenges posed by low-resolution Hi-C data. TRUHiC leverages deep generative modeling to computationally enhance Hi-C contact map resolution, enabling more precise characterization of 3D chromatin structures across diverse biological contexts. Taken together, this dissertation presents an integrative multi-omics framework for studying SVs in the context of 3D genome organization, gene regulation, and disease association. By combining bioinformatics analysis, statistical methods, and computational approaches, particularly Generative AI, this work advances our understanding of how structural variation influences chromatin architecture and gene expression, with broad implications for human genomics, translational medicine, and functional genomics research.
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