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Interpreting type 1 diabetes risk with genetics and single-cell epigenomics.

ABSTRACT: Genetic risk variants that have been identified in genome-wide association studies of complex diseases are primarily non-coding¹. Translating these risk variants into mechanistic insights requires detailed maps of gene regulation in disease-relevant cell types². Here we combined two approaches: a genome-wide association study of type 1 diabetes (T1D) using 520,580 samples, and the identification of candidate cis-regulatory elements (cCREs) in pancreas and peripheral blood mononuclear cells using single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) of 131,554 nuclei. Risk variants for T1D were enriched in cCREs that were active in T cells and other cell types, including acinar and ductal cells of the exocrine pancreas. Risk variants at multiple T1D signals overlapped with exocrine-specific cCREs that were linked to genes with exocrine-specific expression. At the CFTR locus, the T1D risk variant rs7795896 mapped to a ductal-specific cCRE that regulated CFTR; the risk allele reduced transcription factor binding, enhancer activity and CFTR expression in ductal cells. These findings support a role for the exocrine pancreas in the pathogenesis of T1D and highlight the power of large-scale genome-wide association studies and single-cell epigenomics for understanding the cellular origins of complex disease.

SUBMITTER: Chiou J

PROVIDER: S-EPMC10560508 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Interpreting type 1 diabetes risk with genetics and single-cell epigenomics.

Chiou Joshua J Geusz Ryan J RJ Okino Mei-Lin ML Han Jee Yun JY Miller Michael M Melton Rebecca R Beebe Elisha E Benaglio Paola P Huang Serina S Korgaonkar Katha K Heller Sandra S Kleger Alexander A Preissl Sebastian S Gorkin David U DU Sander Maike M Gaulton Kyle J KJ

Nature 20210519 7863

Genetic risk variants that have been identified in genome-wide association studies of complex diseases are primarily non-coding<sup>1</sup>. Translating these risk variants into mechanistic insights requires detailed maps of gene regulation in disease-relevant cell types<sup>2</sup>. Here we combined two approaches: a genome-wide association study of type 1 diabetes (T1D) using 520,580 samples, and the identification of candidate cis-regulatory elements (cCREs) in pancreas and peripheral blood m ...[more]

PMID: 34012112

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Project description:BackgroundDiabetes is one of the greatest public health challenges worldwide, and we still lack complementary approaches to significantly enhance the efficacy of preventive and therapeutic approaches. Genetic and environmental factors are the culprits involved in diabetes risk. Evidence from the last decade has highlighted that deregulation in the immune and inflammatory responses increase susceptibility to type 1 and type 2 diabetes. Spatiotemporal patterns of gene expression involved in immune cell polarisation depend on genomic enhancer elements in response to inflammatory and metabolic cues. Several studies have reported that most regulatory genetic variants are located in the non-protein coding regions of the genome and particularly in enhancer regions. The progress of high-throughput technologies has permitted the characterisation of enhancer chromatin properties. These advances support the concept that genetic alteration of enhancers may influence the immune and inflammatory responses in relation to diabetes.Scope of reviewResults from genome-wide association studies (GWAS) combined with functional and integrative analyses have elucidated the impacts of some diabetes risk-associated variants that are involved in the regulation of the immune system. Additionally, genetic variant mapping to enhancer regions may alter enhancer status, which in turn leads to aberrant expression of inflammatory genes associated with diabetes susceptibility. The focus of this review was to provide an overview of the current indications that inflammatory processes are regulated at the genetic and epigenomic levels in diabetes, along with perspectives on future research avenues that may improve understanding of the disease.Major conclusionsIn this review, we provide genetic evidence in support of a deregulated immune response as a risk factor in diabetes. We also argue about the importance of enhancer regions in the regulation of immune cell polarisation and how the recent advances using genome-wide methods for enhancer identification have enabled the determination of the impact of enhancer genetic variation on diabetes onset and phenotype. This could eventually lead to better management plans and improved treatment responses in human diabetes.

Project description:BackgroundGenome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell-specific manner. Here, we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D.ResultsUsing 3DFAACTS-SNP, we identified from a list of 1228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell type-specific chromosome conformation capture data in 3DFAACTS-SNP identified 266 regulatory regions and 47 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 16 Treg-centric candidate variants and 60 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (> 10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 9376 candidate variants and 4968 candidate target genes, generating a list of potential sites for future T1D or other autoimmune disease research.ConclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function, and illustrate the power of using cell type-specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

Dataset Information

Interpreting type 1 diabetes risk with genetics and single-cell epigenomics.

Publications

Interpreting type 1 diabetes risk with genetics and single-cell epigenomics.

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