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Leveraging Functional Characteristics and Sequence-Dependent DNA Topology in Genomic Association Studies

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Genetic association studies have had great success in identifying loci associated with a range of phenotypes and disease. However, there remains much work to identify and understand the specific genetic drivers. Rapid development of high-throughput functional technologies and methods have afforded many opportunities to better leverage genetic-association data to find and elucidate the molecular causes. This dissertation explores methods that incorporate functional information into genomic association studies. Chapter II covers the largest genomic association study in Systemic Lupus Erythematosus, to date. This study analyzed three ancestries and leveraged differences in LD through a transancestral meta-analysis to narrow regions of SLE-association. In this study, we also imputed Human Leukocyte Antigen (HLA) alleles from SNP data and through a clustering approach identified an amino acid signature that conferred risk in the DRB1 peptide binding pocket. Functional information such as eQTLs and CpG occurrence (if a SNP creates or disrupts a Cytosine-phosphate-Guanine sequence) were also incorporated into the results. Chapter III focuses on an HLA analysis in a family study of Congenital Heart Block (CHB). Again, HLA alleles were imputed from SNP data, and by utilizing information regarding established HLA*C and killer-cell immunoglobulin-like receptor (KIR) interactions, we identified a statistically significant distinction between CHB-affected and unaffected siblings. In Chapter IV we explored using sequence-dependent DNA topology as a novel prioritization metric in genomic association studies. We applied two fine-mapping approaches with SNPs weighted by minor groove width to identify potentially causal SNPs. Other measures of sequence-dependent topology are explored in Appendix I. This novel application of DNA shape in genetic association studies lays the foundation for a research program that includes a number of future studies and methods development, many of which are outlined in Chapter VI. Chapter V presents FALCAN (FAst LoCus ANnotator), an online platform for efficient annotation of genomic variants. Together, the works in this dissertation explore methods and approaches in applying functional information and annotations to genomic association studies.
Autoimmune Disease
Big Data
Genetic Association Studies
Ainsworth, Hannah Christine (author)
Langefeld, Carl D (committee chair)
Alexander-Miller, Martha A (committee member)
Espeland, Mark A (committee member)
Freedman, Barry I (committee member)
Howard, Timothy D (committee member)
2019-09-05T08:35:24Z (accessioned)
2019-09-05T08:35:24Z (available)
2019 (issued)
Molecular Genetics & Genomics (discipline)
http://hdl.handle.net/10339/94317 (uri)
en (iso)
Wake Forest University
Leveraging Functional Characteristics and Sequence-Dependent DNA Topology in Genomic Association Studies

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