OR05-1 Genetic Discovery and Translational Decision Support from Exome Sequencing of 45,231 Type 2 Diabetes Cases and Controls from Five Ancestries

Abstract

Analysis of human exome sequences, the protein-coding regions of genomes, has the potential to identify variation in genes relevant to disease for expedited clinical and therapeutic translation. Here we present results from a massive-scale type 2 diabetes (T2D) exome sequencing project. We analyzed exomes from 20,791 participants with T2D and 24,440 controls from five ancestries (Hispanic/Latino 33.8%, European 24.6%, African-American 13.9%, East Asian 14.1%, South Asian 13.6%) with mean depth 40x, and conducted both single-variant and gene-level association testing. Eighteen single-variant signals reached exome-wide significance (p< 4.3×10-7); all were common variants (minor allele frequency [MAF] > 5%) except MC4R p.Ile269Asn, which was almost exclusively seen in Hispanic/Latinos with MAF 0.89% and T2D OR=2.17 (95% CI: 1.63-2.89) in that population. Only one single-variant association represented a previously unknown T2D genetic locus (SFI1 p.Arg724Trp), but this finding failed to replicate in an independent cohort. We identified gene-level associations composed of rare (MAF < 0.5%) variants (a) in three genes each reaching a gene-level exome-wide significance threshold of 6.57×10-7 (MC4R, PAM, and SLC30A8), including a T2D protective series of >30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to known T2D drug targets (p=6.1×10-3) and candidate genes from knockout mice (p=5.2×10-3). These aggregate gene-level associations suggest that larger sample sizes will allow more refined identification of novel T2D genes, including potential new drug targets. Based on the rare-variant gene-level effect sizes we observed in established T2D drug targets, we estimate that 110K-180K sequenced cases would be required for reliable identification of new T2D drug targets. Additionally, we have developed a Bayesian framework using our association results to facilitate prioritization of genes for translational decision support.