scholarly journals 3DFAACTS-SNP: Using Regulatory T Cell-specific Epigenomics Data to Uncover Candidate Mechanisms of Type-1 Diabetes (T1D) risk

2021 ◽  
Author(s):  
Ning Liu ◽  
Timothy Sadlon ◽  
Ying Ying Wong ◽  
Stephen Martin Pederson ◽  
James Breen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Functional Annotation ◽  
Target Genes ◽  
Association Studies ◽  
Regulatory Function ◽  
Candidate Snps ◽  
Cell Type ◽  
Candidate Target ◽  
Cell Type Specific

Abstract 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 1,228 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 119 regulatory regions and 51 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 17 Treg-centric candidate variants and 35 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 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune 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.

scholarly journals 3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) risk

2020 ◽  
Author(s):  
Ning Liu ◽  
Timothy Sadlon ◽  
Ying Ying Wong ◽  
Stephen Pederson ◽  
James Breen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Immune Tolerance ◽  
Functional Annotation ◽  
Target Genes ◽  
Regulatory Elements ◽  
Regulatory Function ◽  
Cell Type ◽  
Candidate Target ◽  
Cell Type Specific

AbstractBackgroundGenome-wide association and fine-mapping studies have enabled the discovery of single nucleotide polymorphisms (SNPs) and other variants that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the SNPs lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression.MethodsAutoimmunity results from a failure of immune tolerance, suggesting that regulatory T cells (Treg) are likely a significant point of impact for this genetic risk, as Treg are critical for immune tolerance. Focusing on T1D as a model of defective function of Treg in autoimmunity, we designed a SNPs filtering workflow called 3 Dimensional Functional Annotation of Accessible Cell Type Specific SNPs (3DFAACTS-SNP) that utilises overlapping profiles of Treg-specific epigenomic data (ATAC-seq, Hi-C and FOXP3-ChIP) to identify regulatory elements potentially driving the effect of variants associated with T1D, and the gene(s) that they control.ResultsUsing 3DFAACTS-SNP we identified 36 SNPs with plausible Treg-specific mechanisms of action contributing to T1D from 1,228 T1D fine-mapped variants, identifying 119 novel interacting regions resulting in the identification of 51 candidate target genes. We further demonstrated the utility of the workflow by applying it to three other fine-mapped/meta-analysed SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of any genetic variation using all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune 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. The 3DFAACTS-SNP workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

scholarly journals A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257265
Author(s):  
Seung-Soo Kim ◽  
Adam D. Hudgins ◽  
Jiping Yang ◽  
Yizhou Zhu ◽  
Zhidong Tu ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Target Genes ◽  
Association Studies ◽  
Regulatory Elements ◽  
Immune Dysregulation ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Gwas Analysis ◽  
Regulatory Variants

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

scholarly journals Cell type–specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes

JCI Insight ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Matthew J. Dufort ◽  
Carla J. Greenbaum ◽  
Cate Speake ◽  
Peter S. Linsley
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Secretion ◽  
Cell Type ◽  
Onset Type ◽  
Cell Type Specific ◽  
Immune Phenotypes ◽  
New Onset

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

2019 ◽  
Author(s):  
Joshua Chiou ◽  
Chun Zeng ◽  
Zhang Cheng ◽  
Jee Yun Han ◽  
Michael Schlichting ◽  
...  
Keyword(s):  
Beta Cell ◽  
Single Cell ◽  
Islet Cell ◽  
Target Genes ◽  
Regulatory Function ◽  
Cell Type ◽  
Cell Clusters ◽  
Risk Variants ◽  
Cell Type Specific ◽  
Accessible Chromatin

AbstractGenetic risk variants for complex, multifactorial diseases are enriched in cis-regulatory elements. Single cell epigenomic technologies create new opportunities to dissect cell type-specific mechanisms of risk variants, yet this approach has not been widely applied to disease-relevant tissues. Given the central role of pancreatic islets in type 2 diabetes (T2D) pathophysiology, we generated accessible chromatin profiles from 14.2k islet cells and identified 13 cell clusters including multiple alpha, beta and delta cell clusters which represented hormone-producing and signal-responsive cell states. We cataloged 244,236 islet cell type accessible chromatin sites and identified transcription factors (TFs) underlying both lineage- and state-specific regulation. We measured the enrichment of T2D and glycemic trait GWAS for the accessible chromatin profiles of single cells, which revealed heterogeneity in the effects of beta cell states and TFs on fasting glucose and T2D risk. We further used machine learning to predict the cell type-specific regulatory function of genetic variants, and single cell co-accessibility to link distal sites to putative cell type-specific target genes. We localized 239 fine-mapped T2D risk signals to islet accessible chromatin, and further prioritized variants at these signals with predicted regulatory function and co-accessibility with target genes. At the KCNQ1 locus, the causal T2D variant rs231361 had predicted effects on an enhancer with beta cell-specific, long-range co-accessibility to the insulin promoter, and deletion of this enhancer reduced insulin gene and protein expression in human embryonic stem cell-derived beta cells. Our findings provide a cell type- and state-resolved map of gene regulation in human islets, illuminate likely mechanisms of T2D risk at hundreds of loci, and demonstrate the power of single cell epigenomics for interpreting complex disease genetics.

scholarly journals Cell-type-specific meQTL extends melanoma GWAS annotation beyond eQTL and informs melanocyte gene regulatory mechanisms

2021 ◽  
Author(s):  
Tongwu Zhang ◽  
Jiyeon Choi ◽  
Ramile Dilshat ◽  
Berglind Ósk Einarsdóttir ◽  
Michael A Kovacs ◽  
...  
Keyword(s):  
Association Studies ◽  
Splice Junction ◽  
Causal Variant ◽  
Susceptibility Genes ◽  
The Cancer Genome Atlas ◽  
Genome Wide Association Studies ◽  
Cell Type ◽  
Candidate Target ◽  
Cancer Genome Atlas ◽  
Cell Type Specific

AbstractWhile expression quantitative trait loci (eQTL) have been powerful in identifying susceptibility genes from genome-wide association studies (GWAS) findings, most trait-associated loci are not explained by eQTL alone. Alternative QTLs including DNA methylation QTL (meQTL) are emerging, but cell-type-specific meQTL using cells of disease origin has been lacking. Here we established an meQTL dataset using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization using meQTL, eQTL, and mRNA splice-junction QTL from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified susceptibility genes at 63% of melanoma GWAS loci. Among three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes are preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTL and melanoma meQTL identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTL identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIPseq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTL.

scholarly journals Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes

2021 ◽  
Vol 17 (9) ◽  
pp. e1009382
Author(s):  
Samantha N. Piekos ◽  
Sadhana Gaddam ◽  
Pranav Bhardwaj ◽  
Prashanth Radhakrishnan ◽  
Ramanathan V. Guha ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
T Lymphocytes ◽  
Genetic Variants ◽  
B Lymphocyte ◽  
Biomedical Data ◽  
Cell Type ◽  
Scoring Method ◽  
Cell Type Specific ◽  
Data Commons

The repurposing of biomedical data is inhibited by its fragmented and multi-formatted nature that requires redundant investment of time and resources by data scientists. This is particularly true for Type 1 Diabetes (T1D), one of the most intensely studied common childhood diseases. Intense investigation of the contribution of pancreatic β-islet and T-lymphocytes in T1D has been made. However, genetic contributions from B-lymphocytes, which are known to play a role in a subset of T1D patients, remain relatively understudied. We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. This increases the speed of analysis by multiple orders of magnitude. We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). Pipeline-identified variants are primarily common, non-coding, poorly conserved, and are of unknown clinical significance. While variants and their chromatin connectivity are cell-type specific, they are associated with well-studied disease genes in T-lymphocytes. Candidates include established variants in the HLA-DQB1 and HLA-DRB1 and IL2RA loci that have previously been demonstrated to protect against T1D in humans and mice providing validation for this method. Others are included in the well-established T1D GRS2 genetic risk scoring method. More intriguingly, other prioritized variants are completely novel and form the basis for future mechanistic and clinical validation studies The BMDC community-based platform can be expanded and repurposed to increase the accessibility, reproducibility, and productivity of biomedical information for diverse applications including the prioritization of cell type-specific disease alleles from complex phenotypes.

scholarly journals Functional annotation of human long noncoding RNAs using chromatin conformation data

2021 ◽  
Author(s):  
Saumya Agrawal ◽  
Tanvir Alam ◽  
Masaru Koido ◽  
Ivan V. Kulakovskiy ◽  
Jessica Severin ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Rna Binding ◽  
Functional Characterization ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Spatial Proximity ◽  
Chromatin Conformation ◽  
Cell Type ◽  
Cell Type Specific ◽  
Rna Domains

AbstractTranscription of the human genome yields mostly long non-coding RNAs (lncRNAs). Systematic functional annotation of lncRNAs is challenging due to their low expression level, cell type-specific occurrence, poor sequence conservation between orthologs, and lack of information about RNA domains. Currently, 95% of human lncRNAs have no functional characterization. Using chromatin conformation and Cap Analysis of Gene Expression (CAGE) data in 18 human cell types, we systematically located genomic regions in spatial proximity to lncRNA genes and identified functional clusters of interacting protein-coding genes, lncRNAs and enhancers. Using these clusters we provide a cell type-specific functional annotation for 7,651 out of 14,198 (53.88%) lncRNAs. LncRNAs tend to have specialized roles in the cell type in which it is first expressed, and to incorporate more general functions as its expression is acquired by multiple cell types during evolution. By analyzing RNA-binding protein and RNA-chromatin interaction data in the context of the spatial genomic interaction map, we explored mechanisms by which these lncRNAs can act.

scholarly journals Partitioning heritability by functional category using GWAS summary statistics

2015 ◽  
Author(s):  
Hilary Kiyo Finucane ◽  
Brendan Bulik-Sullivan ◽  
Alexander Gusev ◽  
Gosia Trynka ◽  
Yakir Reshef ◽  
...  
Keyword(s):  
Association Studies ◽  
Smoking Behavior ◽  
Complex Diseases ◽  
New Method ◽  
Age At Menarche ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Cell Type ◽  
Genome Wide ◽  
Cell Type Specific

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits spanning a total of 1.3 million phenotype measurements. To enable this analysis, we introduce a new method for partitioning heritability from GWAS summary statistics while controlling for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior. These results demonstrate that GWAS can aid in understanding the biological basis of disease and provide direction for functional follow-up.

scholarly journals Calling differential DNA methylation at cell-type resolution: addressing misconceptions and best practices

2021 ◽  
Author(s):  
Elior Rahmani ◽  
Brandon Jew ◽  
Regev Schweiger ◽  
Brooke Rhead ◽  
Lindsey A. Criswell ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Best Practices ◽  
Regression Model ◽  
Association Studies ◽  
Composition Analysis ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Interaction Terms ◽  
Mathematical Explanations ◽  
Cell Type Specific

AbstractWe benchmarked two approaches for the detection of cell-type-specific differential DNA methylation: Tensor Composition Analysis (TCA) and a regression model with interaction terms (CellDMC). Our experiments alongside rigorous mathematical explanations show that TCA is superior over CellDMC, thus resolving recent criticisms suggested by Jing et al. Following misconceptions by Jing and colleagues with modelling cell-type-specificity and the application of TCA, we further discuss best practices for performing association studies at cell-type resolution. The scripts for reproducing all of our results and figures are publicly available at github.com/cozygene/CellTypeSpecificMethylationAnalysis.

scholarly journals Transcribed enhancers in the human brain identify novel disease risk mechanisms

2021 ◽  
Author(s):  
Pengfei Dong ◽  
Gabriel E. Hoffman ◽  
Pasha Apontes ◽  
Jaroslav Bendl ◽  
Samir Rahman ◽  
...  
Keyword(s):  
Human Brain ◽  
Disease Risk ◽  
Association Studies ◽  
Population Level ◽  
Cell Type ◽  
Level Variation ◽  
Functional Interpretation ◽  
Neuropsychiatric Diseases ◽  
Order Of Magnitude ◽  
Cell Type Specific

Enhancer RNAs (eRNAs) constitute an important tissue- and cell-type-specific layer of the regulome. Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding the population-level variation of eRNAs in the human brain. We jointly analyzed cell type-specific transcriptome and regulome data to identify 30,795 neuronal and 23,265 non-neuronal eRNAs, expanding the catalog of known human brain eRNAs by an order of magnitude. Examination of the population-level variation of the transcriptome and regulome in 1,382 brain samples identified reproducible changes affecting cis- and trans-co-regulation of eRNA-gene modules in schizophrenia. We show that 13% of schizophrenia heritability is jointly mediated in cis by brain gene and eRNA expression. Inclusion of eRNAs in transcriptome-wide association studies facilitated fine-mapping and functional interpretation of disease loci. Overall, our study characterizes the eRNA-gene regulome and genetic mechanisms in the human cortex in both healthy and disease states.

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