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Title: Transcriptional mechanisms influencing glycaemic traits and risk of type 2 diabetes
Author: Payne, Anthony Joseph
ISNI:       0000 0004 7966 1513
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2018
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Genome-wide association studies (GWASs) have identifified many genetic variants associated with type 2 diabetes (T2D) risk and glycaemic traits, most of which are noncoding. Although existing islet RNA sequencing (RNA-seq) studies have identified genes regulated by T2D-associated variants, sample sizes are small, and most known regulatory targets are messenger RNAs RNAs). The work presented in this thesis aims to characterise long noncoding RNA (lncRNA) expression in islets, identify candidate lncRNA regulatory mechanisms, and through incorporation of genetic data, identify genes with potential causal mediation of T2D-associated genetic variation. Using data from 43 GTEx tissues, I first showed that lncRNAs are more tissue specific than mRNAs. Using 38 human islet samples with paired ribosomal rRNA depletion (RD) and poly-A-tailed RNA selection (PA) RNA-seq, I then showed that RD more comprehensively isolates lncRNAs, but the relative effective sequencing depth is approximately half that of PA. Using RD RNA-seq from 54 islet samples, I identified previously unannotated expressed regions, but comparison to lncRNAs from recently published islet-specific annotations showed high discordance between studies. Thus, although RD better captures lncRNAs, its use in transcriptome-wide islet analyses is not practical until sample sizes increase. By analysing differential expression (DE) by T2D status in islets (n = 207) and 48 tissues from GTEx, I found 89 unique lncRNAs with DE in at least one GTEx tissue, and 75 with DE in islets. To give these functional context, co-expression modules were constructed in each tissue with lncRNAs and mRNAs. One common gene module between pancreas and islet was enriched for lncRNAs with DE, and contained mRNAs associated with insulin secretion regulation, ultimately pointing to lncRNA CTB-12O2.1 as a candidate regulator of T2D-relevant gene expression. I next incorporated genetic data with islet RNA-seq (n = 426) to demonstrate a modelling approach for identifying sparse, robust genetic models of gene expression. My approach is replicable, robust, and provides models that are sparser and explain more expression variation than LASSO or elastic net models. Using these models along with expression quantitative trait locus models, I identified 33 candidate expression mediators of genetic associations with T2D and glycaemic traits. Amongst these were two lncRNAs that were transcriptionally associated with biologically relevant mRNAs, making them candidate regulatory targets of corresponding T2D-associated noncoding genetic variants. The data presented throughout has therefore implicated previously unstudied lncRNAs in T2D risk and the regulation of insulin secretion, and has demonstrated how integration of disease-focussed islet RNA-seq and large-scale public datasets such as GTEx and GWAS meta-analyses can effectively identify novel regulatory mechanisms underlying genetic associations with T2D.
Supervisor: McCarthy, Mark I. ; Lindgren, Cecilia M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: type 2 diabetes ; genetics ; transcriptomics