Type 2 diabetes (T2D) has a genetic component which is only partially understood. The majority of genetic variance in disease susceptibility is unaccounted for, whilst the precise transcripts and molecular mechanisms through which most risk variants exert their effect is unclear. A complete understanding of T2D susceptibility mechanisms could have benefits in risk prediction, and in drug discovery through the identification of novel therapeutic targets. Work presented in this thesis aims to define relevant transcripts and disease mechanisms at known susceptibility loci, and to identify disease association with classes of genetic variation other than common single nucleotide polymorphisms (SNPs). KCNQ1 contains intronic variants associated with T2D susceptibility and β-cell dysfunction, but only maternally-inherited alleles confer increased disease risk. It maps within an imprinted domain with an established role in congenital and islet-specific growth phenotypes. Using human adult islet and foetal pancreas samples, I refined the transcripts and developmental stage at which T2D susceptibility must be conferred by demonstrating developmentally plastic monoallelic and biallelic expression. I identified a potential risk mechanism through the effect of T2D risk alleles upon DNA methylation. The disease-associated regions identified through genome-wide association (GWA) studies often contain multiple transcripts. I performed mRNA expression profiling of genes within loci associated with raised proinsulin/insulin ratios in human islets and metabolically relevant tissues. Some genes (notably CT62) were not expressed and therefore excluded from consideration for a risk effect, whilst others (for example C2CD4A) were highlighted as good regional candidates due to specific expression in relevant tissues. GWA studies for T2D risk have focused predominantly upon common single nucleotide polymorphisms. As part of a consortium conducing GWA analysis for copy number variation (CNV) and T2D risk, I optimised and compared alternative methods of CNV genotyping, before using this information to validate two signals of disease association. I genotyped three rare single nucleotide variants emerging from an association study with T2D risk based on imputed data, providing an indication of imputation accuracy and more powerful disease association analysis. These data underscore the challenge of translating association signals to causal mechanisms, and of identifying alternative forms of genomic variation which contribute to T2D risk. My work highlights candidates for functional analysis around proinsulin-associated loci, and makes significant progress towards uncovering risk mechanisms at the KCNQ1 locus.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:581219 |
| Date | January 2013 |
| Creators | Travers, Mary E. |
| Contributors | McCarthy, Mark I.; Gloyn, Anna L. |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:d99892d8-534a-4908-b5dc-ab1d8b1cab52 |
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