Shared genetic risk contributes to type 1 and type 2 diabetes etiology

Aylward, Anthony; Chiou, Joshua; Okino, Mei-Lin; Kadakia, Nikita; Gaulton, Kyle J.

doi:10.1093/hmg/ddy314

Cited by 52 publications

(67 citation statements)

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“…However, these enrichments based on ensemble data obscure the potential role of islet cell type- and state-specific regulation in these traits. Using our islet cell type- and state-resolved accessible chromatin profiles, we sought to determine the enrichment of genetic variants associated with type 1 and 2 diabetes 10,54 and diabetes-related quantitative phenotypes 13,55–59 as well as other complex traits and disease for calibration 60–67 . We first determined the enrichment of variants in accessible chromatin sites for each islet cell type and state using stratified LD score regression 68,69 (see Methods).…”

Section: Resultsmentioning

confidence: 99%

Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

Chiou

Zeng

Zhang

et al. 2019

Preprint

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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...

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Section: Resultsmentioning

confidence: 99%

Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

Chiou

Zeng

Zhang

et al. 2019

Preprint

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“…ATAC-seq footprinting was performed as previously described (Aylward et al, 2018). In brief, diploid genomes for CyT49 were created using vcf2diploid (version 0.2.6a) (Rozowsky et al, 2011) and genotypes called from whole genome sequencing and scanned for a compiled database of TF sequence motifs from JASPAR (Mathelier et al, 2016) and ENCODE (Consortium, 2012) with FIMO (version 4.12.0) (Grant et al, 2011) using default parameters for p-value threshold and a 40.9% GC content based on the hg19 human reference genome.…”

Section: Atac-seq Footprinting Analysismentioning

confidence: 99%

Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development

Geusz

Wang

Chiou

et al. 2020

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Genetic variants associated with type 2 diabetes (T2D) risk affect gene regulation in metabolically relevant tissues, such as pancreatic islets. Here, we investigated contributions of regulatory programs active during pancreatic development to T2D risk.Generation of chromatin maps from developmental precursors throughout pancreatic differentiation of human embryonic stem cells (hESCs) identifies enrichment of T2D variants in pancreatic progenitor-specific stretch enhancers that are not active in islets.Genes associated with progenitor-specific stretch enhancers are predicted to regulate developmental processes, most notably tissue morphogenesis. Through gene editing in hESCs, we demonstrate that progenitor-specific enhancers harboring T2D-associated variants regulate cell polarity genes LAMA1 and CRB2. Knockdown of lama1 or crb2 in zebrafish embryos causes a defect in pancreas morphogenesis and impairs islet cell development. Together, our findings reveal that a subset of T2D risk variants specifically affects pancreatic developmental programs, suggesting that dysregulation of developmental processes can predispose to T2D. This work was supported by grant T32 GM008666 (R.J.G.), grant P30 DK064391 (K.J.), R01 DK068471 and U01 DK105541 (M.S., B.R.).

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“…Uncovering colocalising signals could provide biological insights into shared disease mechanisms, and, in the case of signals that increase the risk of both type 1 and type 2 diabetes, potentially reveal therapeutic targets effective for both diseases. A recent analysis suggested that the same genetic variant is altering risk of both type 1 and type 2 diabetes in five regions, near Centromere Protein W ( CENPW ), Chymotripsinogen B1 ( CTRB1 )/Breast Cancer AntiEstrogen Resistance Protein 1 ( BCAR1 ), GLIS Family Zinc Finger Protein 3 ( GLIS3 ), B-cell Lymphoma 11A ( BCL11A ) and Thyroid Adenoma-Associated Protein ( THADA ) 3 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of overlapping genetic association in type 1 and type 2 diabetes

Inshaw

Sidore

Cucca

et al. 2020

Preprint

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26Aims/hypothesis: Given the potential shared aetiology between type 1 and type 27 2 diabetes, we aimed to identify any genetic regions associated with both 28 diseases. For associations where there is a shared signal and the allele that 29 increases risk to one disease also increases risk to the other, inference about 30 shared aetiology could be made, with the potential to develop therapeutic 31 strategies to treat or prevent both diseases simultaneously. Alternatively, if a 32 genetic signal colocalises with divergent effect directions, it could provide 33 valuable biological insight into how the association affects the two diseases 34 differently. 35Methods: Using publicly available type 2 diabetes summary statistics from a 36 meta-analysis of European ancestry individuals (74,124 cases and 824,006 37 controls) and type 1 diabetes summary statistics from a meta-analysis of studies 38 on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we 39 identified all regions of 0.5 Mb that contained variants associated with both 40 diseases (false discovery rate<0.01). In each region, we performed forward 41 stepwise logistic regression to identify independent association signals, then 42 examined colocalisation of each type 1 diabetes signal with each type 2 diabetes 43 signal using coloc. Any association with a colocalisation posterior probability of 44 ≥0.9 was considered a genuine shared association with both diseases. 45

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Shared genetic risk contributes to type 1 and type 2 diabetes etiology

Cited by 52 publications

References 0 publications

Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

Single cell chromatin accessibility reveals pancreatic islet cell type- and state-specific regulatory programs of diabetes risk

Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development

Analysis of overlapping genetic association in type 1 and type 2 diabetes

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