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Transcription factors can bind cis-regulatory DNA elements to achieve their regulatory properties. Identification of transcription factor binding sites remains a crucial goal in deciphering transcriptional regulatory circuits. The vast majority of genetic variants identified from whole genome sequencing studies and leading disease-causing SNPs implicated in genome-wide association studies (GWAS) lie well outside of protein coding regions. The functional effect of variants within non-coding sequence is often through creation or disruption of individual transcription factor binding that alters downstream gene regulatory activity. In addition, transcription factors can act cooperatively to regulate transcription in a context-specific manner. Some binding complexes, such as CTCF together with cohesin proteins, can also mediate 3D chromatin interactions that also have downstream gene regulatory control. My dissertation is focused on deciphering these interactions driving gene regulatory circuits. In this dissertation, I develop an improved footprinting algorithm to map transcription factor binding sites genome-wide, study regulatory variants associated with transcription factor binding affinity, and explore transcription factor cooperativity and their role in 3D chromatin interaction. In Chapter 2, I will introduce the TRACE algorithm, a multi-threaded computational footprinting method to predict transcription factor binding sites, using chromatin accessibility data (DNase-seq or ATAC-seq) and sequence information. In the development of the method, I Implemented a multivariate hidden Markov model (HMM) in an unsupervised training manner for identifying and labeling DNase footprints. TRACE exhibited the best overall performance among all existing footprinting methods after a comprehensive evaluation. In Chapter 3, I investigated the association between genetic variants and transcription factor binding activity to identify footprint QTLs (fpQTLs) at a base pair resolution, contributing to a better knowledge of the mechanism behind the linkage between genotypic variation and gene regulation as well as disease phenotypes. Overall, detection of fpQTLs provides additional information for a more complete characterization of the landscape of human regulatory variation and its direct effect on gene expression. In Chapter 4, I employed an artificial neural network called Self-Organizing Maps (SOMs) to identify “clusters” of transcription factors and define co-binding patterns. I specifically examined the transcription factor enrichment at chromatin loop anchors and studied how they might modulate downstream looping effects. Together, the studies in this dissertation provide improved transcription factor binding site prediction, deliver improved functional interpretation of noncoding variation, and expand our knowledge on transcription factor cooperativity and their effect on 3D organization of chromatin.

Transcriptional enhancer elements are essential in the establishment and maintenance of gene expression programs that define cellular identity and function. DNA variants that predispose to common disease, identified by genome-wide association studies (GWAS), frequently lie in enhancer elements. Interpreting the impact of DNA variants on enhancer function and transcriptional output is key to determining the role of the disease-associated variants in the pathogenesis of disease. Here we present PreSTIGE (Predicting Specific Tissue Interactions of Genes and Enhancers), a method that identifies the specific gene targets of enhancers, and apply this methodology to delineate the impact of disease-associated variants on enhancer function. We demonstrate that for six autoimmune traits, GWAS association signals often arise from multiple polymorphisms in linkage disequilibrium (LD) that map to clusters of active enhancer elements. This result suggests a “multiple enhancer variant” hypothesis, in which several variants in LD alter enhancer function and cooperatively affect transcript levels. Multiple enhancer variants within a given locus are determined by PreSTIGE to target the same gene. To address the question of whether additional variants within a target gene’s regulatory circuitry can alter the transcriptional effect of the locus, we identified variants that are inherited independently of known GWAS risk loci and interact with disease-associated variants. The interacting SNPs lie in enhancer elements that are constituents of the regulatory circuitry of the same gene. These variants, termed “outside variants” functionally modify the effect of the risk variants on target transcript levels and ultimately alter clinical risk. To further our understanding of the regulatory circuitry that dictates gene expression levels, we evaluated how transcriptional control is established and altered during early embryogenesis. We found that the regulatory circuitry of genes expressed similarly in two sequential stages of pluripotency undergoes extensive rewiring as naive stem cells transition into pluripotent stem cells primed for differentiation. This rewiring appropriates transcriptional control to a new set of enhancers, which we term “seed” enhancers. Seed enhancers are more frequently utilized in downstream somatic tissues and often expand into enhancer clusters. Taken together, these results demonstrate how the multiple enhancer elements that comprise the regulatory circuitry of a gene cooperatively control transcription in normal development and human disease. Our results indicate that the impact of disease-associated loci is frequently reliant on the combinatorial effects of multiple variants that perturb enhancers within the same regulatory circuit.

Genetic adaptations to local environment during evolution shaped the human genome. Identifying evolutionarily important genetic variants is clinically significant because the mismatch between our slow-evolving genome and the cultural upheaval underlies many diseases of civilization. Recent sequencing technology advances start a Genomic era and promise to elucidate the genetic basis of human health and disease. While most attention had been drawn to protein-coding genes in the nuclear genome, regulatory regions and mitochondrial genome were much less studied. My research aims to investigate the evolutionary and clinical significance of these two categories. Starting my research, I summarized the latest advances in understanding the role of nutrition in human genome evolution and introduced to the Nutrition field population genomics approaches to identify dietary adaptions. My first research project examined the adaptation of regulatory variants to 42 environmental factors. With a newly developed environmental correlation method, I found that expression QTLs are enriched in signals of environmental adaptation and regulatory adaptation are especially important for some environmental factors, like climate, and for some biological pathways, including immune and metabolic pathways. My second project was a case study to test a hypothesis that an Asian-common haplotype was adaptive to the plant-based diet in Asia by enhancing non-heme iron absorption. With an iron absorption study involving 57 Asian women volunteers, I found that consistent to our hypothesis homozygous carriers of the adaptive haplotype absorbed 22% more non-heme iron than non-carriers. Intriguingly, I also observed that compared to Caucasian women, Asian women absorbed more non-heme iron even in face of higher iron store. My third project utilized the next-generation sequencing data from the 1000 Genomes Project and investigated the prevalence and clinical relevance of mitochondrial DNA (mtDNA) heteroplasmy, the presence of multiple versions of mtDNA in a cell. I found that about 90% healthy individuals carry at least one heteroplasmies and at least 20% individuals harbor disease-associated heteroplasmies. I demonstrated that heteroplasmic mutations are highly pathogenic and subject to weak negative selection. My research suggests that heteroplasmic mutations may drift to high frequency across life-span and contribute to age-related diseases.