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Single nucleus ATAC-seq (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. As pancreatic islets are central to type 2 diabetes (T2D), we profiled 15.3k islet cells using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. We observed state-specific enrichment of fasting glucose and T2D GWAS for beta cells as well as enrichment for other endocrine cell types. At T2D signals localized to islet accessible chromatin, we prioritized variants with predicted regulatory function and co-accessibility with target genes. A causal T2D variant rs231361 at the KCNQ1 locus had predicted effects on a beta cell enhancer co-accessible with INS , and genome editing in embryonic stem cell-derived beta cells affected INS levels. Together our findings demonstrate the power of single cell epigenomics for interpreting complex disease genetics.

Genetic variants affecting pancreatic islet enhancers are central to T2D risk, but the gene targets of islet enhancer activity are largely unknown. We generate a high-resolution map of islet chromatin loops using Hi-C assays in three islet samples and use loops to annotate target genes of islet enhancers defined using ATAC-seq and published ChIP-seq data. We identify candidate target genes for thousands of islet enhancers, and find that enhancer looping is correlated with islet-specific gene expression. We fine-map T2D risk variants affecting islet enhancers, and find that candidate target genes of these variants defined using chromatin looping and eQTL mapping are enriched in protein transport and secretion pathways. At IGF2BP2 , a fine-mapped T2D variant reduces islet enhancer activity and IGF2BP2 expression, and conditional inactivation of IGF2BP2 in mouse islets impairs glucose-stimulated insulin secretion. Our findings provide a resource for studying islet enhancer function and identifying genes involved in T2D risk.

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43Genetic risk variants for complex, multifactorial diseases are enriched in cis-regulatory elements. 44Single cell epigenomic technologies create new opportunities to dissect cell type-specific 45 mechanisms of risk variants, yet this approach has not been widely applied to disease-relevant 46 tissues. Given the central role of pancreatic islets in type 2 diabetes (T2D) pathophysiology, we 47 generated accessible chromatin profiles from 14.2k islet cells and identified 13 cell clusters 48 including multiple alpha, beta and delta cell clusters which represented hormone-producing and 49 signal-responsive cell states. We cataloged 244,236 islet cell type accessible chromatin sites and 50 identified transcription factors (TFs) underlying both lineage-and state-specific regulation. We 51 measured the enrichment of T2D and glycemic trait GWAS for the accessible chromatin profiles 52 of single cells, which revealed heterogeneity in the effects of beta cell states and TFs on fasting 53 glucose and T2D risk. We further used machine learning to predict the cell type-specific regulatory 54 function of genetic variants, and single cell co-accessibility to link distal sites to putative cell type-55 specific target genes. We localized 239 fine-mapped T2D risk signals to islet accessible 56 chromatin, and further prioritized variants at these signals with predicted regulatory function and 57 co-accessibility with target genes. At the KCNQ1 locus, the causal T2D variant rs231361 had 58 predicted effects on an enhancer with beta cell-specific, long-range co-accessibility to the insulin 59 promoter, and deletion of this enhancer reduced insulin gene and protein expression in human 60 embryonic stem cell-derived beta cells. Our findings provide a cell type-and state-resolved map 61 of gene regulation in human islets, illuminate likely mechanisms of T2D risk at hundreds of loci, 62 and demonstrate the power of single cell epigenomics for interpreting complex disease genetics. 63 64 65 66 67 68 69 70 71 72 73 Gene regulatory programs are largely orchestrated by cis-regulatory elements that direct the 74 expression of genes in response to specific developmental and environmental cues. Genetic 75 variants associated with disease by genome-wide association studies (GWAS) are highly 76 enriched within putative cis-regulatory elements 1 , highlighting the importance of regulatory 77 sequence in mediating disease risk. The activity of regulatory elements is often restricted to 78 specific cell types and/or cell states, limiting the ability of ATAC-seq and other "ensemble" (or 79 "bulk") epigenomic technologies to map regulatory elements in individual cell types within disease-80 relevant tissues. To overcome this limitation, new approaches to obtain ATAC-seq profiles from 81 single nuclei (snATAC-seq) allow for the disaggregation of open chromatin from heterogenous 82 samples into component cell types and subtypes 2-5 . These developments create opportunities to 83dissect the molecular mechanisms that underlie genetic risk of disease. How...