Looking beyond the standard genome-wide association study: Biologically-motivated methodological approaches to discover novel genetic variants associated with complex human traits and disease

  • Author / Creator
    Im, Cindy
  • The standard approach for testing associations between common single nucleotide genetic variants (referred to as single nucleotide polymorphisms or SNPs) and disease entails testing disease associations for each SNP in the genome individually. This “hypothesis-free” approach has identified thousands of statistically significant associations between single SNPs and a wide range of diseases. However, complex forms of genetic variation – which include epistatic interactions, gene-environment interactions, inheritance patterns, rare variants, and structural variants – represent a tremendous potential source of transcriptional complexity in the human genome and may contribute substantially to disease risk. These complex forms of genetic variation are not explored in conventional single-SNP genome-wide association studies, largely due to computational, methodological, and statistical constraints. In this dissertation, we look beyond the contributions of single SNPs to the genetic architecture of disease and consider novel approaches to investigate how untested classes of genomic variation present in the human genome may advance our understanding of the genetic basis of disease. More specifically, the studies presented in this thesis describe a novel methodological framework to detect patterns of epistasis (multiple SNP interactions) and haplotypes (SNP alleles arranged on the same chromosome) associated with complex disease traits that may also potentially model the regulation of trait-related gene transcription events, thereby elucidating central biomolecular mechanisms that influence disease trait pathogenesis. This methodological framework may be summarized as follows: first, a “filter” is employed to restrict the set of investigated SNPs to those with putative biological functions; subsequently, a novel, non-exhaustive statistical approach is implemented to discover candidate epistatic interaction and haplotype associations with disease traits among filtered SNPs. As a final step, replication and biological inference analyses are conducted to assess the credibility of complex genetic variant discoveries. Under this framework, we increase the prior probability of identifying epistatic interactions or haplotypes that are transcriptionally relevant, and facilitate searches of the large space of interactions/haplotypes without limiting the number of tested associations using computational burden-based criteria to improve power. Our results demonstrate the relevance of studies of epistasis in explaining the variability of bone mineral density (an integral determinant of bone health) in adult survivors of pediatric cancer exposed to bone-diminishing treatments, and the effects of haplotypes on risk for primary biliary cholangitis (an incurable autoimmune disease of the liver) in Japanese. We suggest that the discovered genetic targets from these analyses be considered for future basic research into biological mechanisms influencing bone mineral density and primary biliary cholangitis, under the expectation that such research will support the eventual objective of developing potential health applications for the prevention, diagnosis, or treatment of these health conditions.

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  • Degree
    Doctor of Philosophy
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