Using Genome-wide Approaches to Characterize the Relationship Between Genomic Variation and Disease: A Case Study in Oligodendroglioma and Staphylococcus arueus

Abstract

<p>Genetic variation is a natural occurrence in the genome that contributes to the phenotypic differences observed between individuals as well as the phenotypic outcomes of various diseases, including infectious disease and cancer. Single nucleotide polymorphisms (SNPs) have been identified as genetic factors influencing host susceptibility to infectious disease while the study of copy number variation (CNV) in various cancers has led to the identification of causal genetic factors influencing tumor formation and severity. In this work, we evaluated the association between genomic variation and disease phenotypes to identify SNPs contributing to host susceptibility in Staphylococcus aureus (<italic>S. aureus</italic>) infection and to characterize a nervous system brain tumor, known as oligodendroglioma (OD), using the CNV observed in tumors with varying degree of malignancy.</p><p>Using SNP data, we utilized a computational approach, known as in silico haplotype mapping (ISHM), to identify genomic regions significantly associated with susceptibility to <italic>S. aureus</italic> infection in inbred mouse strains. We conducted ISHM on four phenotypes collected from <italic>S. aureus</italic> infected mice and identified genes contained in the significant regions, which were considered to be potential candidate genes. Gene expression studies were then conducted on inbred mice considered to be resistant or susceptible to <italic>S. aureus</italic> infection to identify genes differentially expressed between the two groups, which provided biological validation of the genes identified in significant ISHM regions. Genes identified by both analyses were considered our top priority genes and known biological information about the genes was used to determine their function roles in susceptibility to <italic>S. aureus</italic> infection.</p><p> We then evaluated CNV in subtypes of ODs to characterize the tumors by their genomic aberrations. We conducted array-based comparative genomic hybridization (CGH) on 74 ODs to generate genomic profiles that were classified by tumor grade, providing insight about the genomic changes that typically occur in patients with OD ranging from the less to more severe tumor types. Additionally, smaller genomic intervals with substantial copy number differences between normal and OD DNA samples, known as minimal critical regions (MCRs), were identified among the tumors. The genomic regions with copy number changes were interrogated for genes and assessed for their biological roles in the tumors' phenotype and formation. This information was used to assess the validity of using genomic variation in these tumors to further classify these tumors in addition to standard classification techniques. </p><p> The studies described in this project demonstrate the utility of using genetic variation to study disease phenotypes and applying computational and experimental techniques to identify the underlying genetic factors contributing to disease pathogenesis. Moreover, the continued development of similar approaches could aid in the development of new diagnostic procedures as well as novel therapeutics for the generation of more personalized treatments. The genomic regions with copy number changes were interrogated for genes and assessed for their biological roles in the tumors' phenotype and formation. This information was used to assess the validity of using genomic variation in these tumors to further classify these tumors in addition to standard classification techniques.</p><p> The studies described in this project demonstrate the utility of using genetic variation to study disease phenotypes and applying computational and experimental techniques to identify the underlying genetic factors contributing to disease pathogenesis. Moreover, the continued development of similar approaches could aid in the development of new diagnostic procedures as well as novel therapeutics for the generation of more personalized treatments.</p>