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

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.

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

JCI Insight ◽

10.1172/jci.insight.125556 ◽

2019 ◽

Vol 4 (4) ◽

Cited By ~ 10

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

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

10.1101/2020.09.04.279554 ◽

2020 ◽

Cited By ~ 1

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.

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

10.21203/rs.3.rs-1175572/v1 ◽

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.

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Genetic Variants Confer Susceptibility to JIA, Type 1 Diabetes, and Celiac Disease

Internal Medicine News ◽

10.1016/s1097-8690(11)70096-8 ◽

2011 ◽

Vol 44 (2) ◽

pp. 50

Author(s):

MICHELE G. SULLIVAN

Keyword(s):

Type 1 Diabetes ◽

Celiac Disease ◽

Genetic Variants

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110-OR: Type 2 Diabetes (T2D)–Associated TCF7L2 Genetic Variants and Indices of Insulin Secretion and Sensitivity in Individuals at Risk for Type 1 Diabetes (T1D)

Diabetes ◽

10.2337/db20-110-or ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 110-OR

Author(s):

MARIA J. REDONDO ◽

MEGAN V. WARNOCK ◽

LAURA E. BOCCHINO ◽

SUSAN GEYER ◽

ALBERTO PUGLIESE ◽

...

Keyword(s):

Type 2 Diabetes ◽

At Risk ◽

Type 1 Diabetes ◽

Insulin Secretion ◽

Genetic Variants

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Type 1 Diabetes Mellitus-Associated Genetic Variants Contribute to Overlapping Immune Regulatory Networks

Frontiers in Genetics ◽

10.3389/fgene.2018.00535 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 10

Author(s):

Denis M. Nyaga ◽

Mark H. Vickers ◽

Craig Jefferies ◽

Jo K. Perry ◽

Justin M. O’Sullivan

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Type 1 Diabetes Mellitus ◽

Genetic Variants ◽

Regulatory Networks

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Modeling Type 1 Diabetes Using Pluripotent Stem Cell Technology

Frontiers in Endocrinology ◽

10.3389/fendo.2021.635662 ◽

2021 ◽

Vol 12 ◽

Author(s):

Kriti Joshi ◽

Fergus Cameron ◽

Swasti Tiwari ◽

Stuart I. Mannering ◽

Andrew G. Elefanty ◽

...

Keyword(s):

Type 1 Diabetes ◽

Stem Cell ◽

Genetic Variants ◽

Genetic Background ◽

Pluripotent Stem Cell ◽

Cell Types ◽

Polygenic Inheritance

Induced pluripotent stem cell (iPSC) technology is increasingly being used to create in vitro models of monogenic human disorders. This is possible because, by and large, the phenotypic consequences of such genetic variants are often confined to a specific and known cell type, and the genetic variants themselves can be clearly identified and controlled for using a standardized genetic background. In contrast, complex conditions such as autoimmune Type 1 diabetes (T1D) have a polygenic inheritance and are subject to diverse environmental influences. Moreover, the potential cell types thought to contribute to disease progression are many and varied. Furthermore, as HLA matching is critical for cell-cell interactions in disease pathogenesis, any model that seeks to test the involvement of particular cell types must take this restriction into account. As such, creation of an in vitro model of T1D will require a system that is cognizant of genetic background and enables the interaction of cells representing multiple lineages to be examined in the context of the relevant environmental disease triggers. In addition, as many of the lineages critical to the development of T1D cannot be easily generated from iPSCs, such models will likely require combinations of cell types derived from in vitro and in vivo sources. In this review we imagine what an ideal in vitro model of T1D might look like and discuss how the required elements could be feasibly assembled using existing technologies. We also examine recent advances towards this goal and discuss potential uses of this technology in contributing to our understanding of the mechanisms underlying this autoimmune condition.

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