Type 2 diabetes (T2D) genetics has realized extraordinary advances, including identification of at least 100 genetic loci at genome-wide significance. However, to convert these loci into targets for new therapies, several gaps in knowledge need to be addressed. Evidence from GWAS and exome sequencing studies strongly suggest that non-coding regulatory variants play a major role in T2D risk, although at most T2D loci, the functional variants, their target genes, the relevant tissues, and the direction of their effect to increase or decrease gene function remain unknown. Allelic heterogeneity and linkage disequilibrium (LD) can make the number of underlying signals and their identities ambiguous. In addition, functional T2D variants may lead to disease through altered insulin secretion, insulin resistance, or other aspects of metabolic risk that may involve several tissues, and the impact of altered gene expression needs to be defined. The overall goal of our proposal is to identify the functional variants, target genes and regulatory mechanisms responsible for noncoding T2D association signals. This proposal builds on our substantial experience with both T2D genetics and functional analyses, including the impressive resources of the METSIM and FUSION studies, the gene regulatory elements we defined in pancreatic islets using chromatin immunoprecipitation (ChIP-seq), DNase hypersensitivity (DNase-seq), and formaldehyde-assisted identification of regulatory elements (FAIRE-seq), and our experimental studies that implicate specific variants, tissues, and directions of effect at the TCF7L2, JAZF1, ARAP1, and CAMK1D T2D loci. We will define the physiological characteristics and multiple signals at T2D loci by testing for association with detailed quantitative traits (QTs) in METSIM and with islet, adipose, and muscle transcript levels and isoforms. Step-wise conditional analysis with simultaneous modeling of multiple loci will be used to define additional association signals, which will be compared with existing trans-ancestry fine-mapping data. We will identify regulatory variants and target genes using signals and allelic imbalances in transcription factor binding, chromatin accessibility, and RNA-seq and miRNA-seq data from T2D-relevant tissues and cells. We will apply an allele-aware pipeline to align sequence data, intersect variants with regulatory elements, and use transcription factor and chromatin allelic imbalances to distinguish likely functional variants from other candidate variants located in regulatory elements. We will identify causal relationships between variants and genes by expression QTs and allelic imbalance, integrated with QT data, and correlation of regulatory elements with expression level across cell types. Finally, we will use experimental studies to identify functional regulatory variants and the molecular mechanisms by which they influence gene and protein activity. Successful completion of these aims will translate T2D association signals into biological insights and therapeutic targets. Pathogenic variants, the mechanisms by which variants affect gene function, and their physiological consequences will be determined, guiding studies that evaluate novel therapies.
Diabetes is a leading cause of morbidity and mortality worldwide and has a strong inherited basis. The proposed work will identify functional DNA variants that influence type 2 diabetes and the mechanisms by which these variants influence gene expression and disease. The results may lead to improved disease diagnosis and treatment.
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