Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

Robertson, Catherine; Jrj., Inshaw; Önengüt-Gümüşcü, Suna; Wm, Chen; Cruz, Daniel Felipe; Yang, Hanzhi; Cutler, Antony J.; Djm., Crouch; Farber, Emily; Bridges, S. Louis; Edberg, Jeffrey C.; Kimberly, Robert P.; Buckner, Jane H.; Deloukas, Panos; Divers, Jasmin; Dabelea, Dana; Lawrence, Jean M.; Marcovina, Santica M.; Shah, Akshay; Greenbaum, Carla J.; Atkinson, Mark A.; Gregersen, Peter K.; Oksenberg,; Pociot, Flemming; Rewers, Marian; Steck, Andrea K.; Dunger, David; Wicker, Linda S.; Concannon, Patrick; Todd, John; Rich, Stephen S.

doi:10.1038/s41588-021-00880-5

Cited by 177 publications

(163 citation statements)

References 101 publications

(133 reference statements)

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“…With respect to the lead cis-pQTL for SomaScan, our finding supports IL7R as a likely causal gene at an established type 1 diabetes locus 26 . More recent work, however, identified two distinct variants (rs2303137 27 and rs2287900 28 , r 2 = 0.29) in the same locus, both in moderate LD (r 2 = 0.45) with the SomaScan cis-pQTL but without evidence for colocalisation. However, there is some orthogonal evidence supporting ILR7 as the candidate causal gene at this locus, including the preliminary success of IL-7Ra antibodies in mouse models of type 1 diabetes 29 and immunomodulation in patients with type 1 diabetes 30 .…”

Section: Resultsmentioning

confidence: 95%

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

et al. 2021

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Affinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques—the aptamer-based SomaScan® v4 assay and the antibody-based Olink assays—to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein–phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer’s disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries.

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Section: Resultsmentioning

confidence: 95%

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

et al. 2021

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“…Overall, approximately 50 loci have been linked to T1D risk. 2 , 3 Despite the genetic predisposition, the rapid rise in the incidence of childhood T1D worldwide, at an estimated average annual increase of 3.9% over the past decades, is too high to result only from genetic causes; 4 , 5 , 6 we also know from monozygotic twins that genetics do not fully explain T1D, given that disease concordance does not reach 100% (30%–70% range). 7 Earlier studies have linked T1D risk to diverse environmental factors, including epidemiological, pathological, and in vitro studies that have implicated enteroviruses as initiators of autoimmunity and β cell failure in genetically susceptible individuals.…”

Section: Introductionmentioning

confidence: 99%

Localization of enteroviral RNA within the pancreas in donors with T1D and T1D-associated autoantibodies

Geravandi

Richardson

Pugliese

et al. 2021

Cell Reports Medicine

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Summary Enteroviral infections have been associated with autoimmunity and type 1 diabetes (T1D), but reliable methods to ascertain localization of single infected cells in the pancreas were missing. Using a single-molecule-based fluorescent in situ hybridization (smFISH) method, we detected increased virus infection in pancreases from organ donors with T1D and with disease-associated autoantibodies (AAb + ). Although virus-positive β cells are found at higher frequency in T1D pancreases, compared to control donors, but are scarce, most virus-positive cells are scattered in the exocrine pancreas. Augmented CD45 + lymphocytes in T1D pancreases show virus positivity or localization in close proximity to virus-positive cells. Many more infected cells were also found in spleens from T1D donors. The overall increased proportion of virus-positive cells in the pancreas of AAb + and T1D organ donors suggests that enteroviruses are associated with immune cell infiltration, autoimmunity, and β cell destruction in both preclinical and diagnosed T1D.

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“…We also rely on the PEP parsers for listing reference genome assets for the refgenie server [ 23 , 24 ]. The PEP specification has been used for a variety of analysis types, such as describing samples for The Cancer Genome Atlas (TCGA) [ 25 ], CRISPR-based screening [ 26 ], DNA methylation analysis [ 27 ], simulated genomic interval data [ 28 ], analysis of Type I diabetes genetics [ 29 ], and others [ 30 ]. A curated list of other research that uses PEP format is maintained in the PEP documentation.…”

Section: Discussionmentioning

confidence: 99%

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

et al. 2021

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Background Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software. Results To address this, we present the Portable Encapsulated Project (PEP) specification, a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many biological samples. In addition to standardization, the PEP specification provides descriptors and modifiers for project-level and sample-level metadata, which improve portability across both computing environments and data processing tools. PEPs include a schema validator framework, allowing formal definition of required metadata attributes for data analysis broadly. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata. Conclusions The PEP specification is an important step toward unifying data annotation and processing tools in data-intensive biological research projects. Links to tools and documentation are available at http://pep.databio.org/.

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Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

Cited by 177 publications

References 101 publications

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling

Localization of enteroviral RNA within the pancreas in donors with T1D and T1D-associated autoantibodies

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

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