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An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

Genome-wide association studies (GWAS) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. Here we present an open resource that provides systematic fine-mapping and gene prioritization across 133,441 published human GWAS loci. We in...

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Autores principales: Mountjoy, Edward, Schmidt, Ellen M., Carmona, Miguel, Schwartzentruber, Jeremy, Peat, Gareth, Miranda, Alfredo, Fumis, Luca, Hayhurst, James, Buniello, Annalisa, Karim, Mohd Anisul, Wright, Daniel, Hercules, Andrew, Papa, Eliseo, Fauman, Eric B., Barrett, Jeffrey C., Todd, John A., Ochoa, David, Dunham, Ian, Ghoussaini, Maya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611956/
https://www.ncbi.nlm.nih.gov/pubmed/34711957
http://dx.doi.org/10.1038/s41588-021-00945-5
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author Mountjoy, Edward
Schmidt, Ellen M.
Carmona, Miguel
Schwartzentruber, Jeremy
Peat, Gareth
Miranda, Alfredo
Fumis, Luca
Hayhurst, James
Buniello, Annalisa
Karim, Mohd Anisul
Wright, Daniel
Hercules, Andrew
Papa, Eliseo
Fauman, Eric B.
Barrett, Jeffrey C.
Todd, John A.
Ochoa, David
Dunham, Ian
Ghoussaini, Maya
author_facet Mountjoy, Edward
Schmidt, Ellen M.
Carmona, Miguel
Schwartzentruber, Jeremy
Peat, Gareth
Miranda, Alfredo
Fumis, Luca
Hayhurst, James
Buniello, Annalisa
Karim, Mohd Anisul
Wright, Daniel
Hercules, Andrew
Papa, Eliseo
Fauman, Eric B.
Barrett, Jeffrey C.
Todd, John A.
Ochoa, David
Dunham, Ian
Ghoussaini, Maya
author_sort Mountjoy, Edward
collection PubMed
description Genome-wide association studies (GWAS) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. Here we present an open resource that provides systematic fine-mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine-mapped genetics and functional genomics data using 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (OR = 8.1, 95% CI: (5.7, 11.5)). These results are publicly available through a web portal (http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.
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spelling pubmed-76119562022-04-28 An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci Mountjoy, Edward Schmidt, Ellen M. Carmona, Miguel Schwartzentruber, Jeremy Peat, Gareth Miranda, Alfredo Fumis, Luca Hayhurst, James Buniello, Annalisa Karim, Mohd Anisul Wright, Daniel Hercules, Andrew Papa, Eliseo Fauman, Eric B. Barrett, Jeffrey C. Todd, John A. Ochoa, David Dunham, Ian Ghoussaini, Maya Nat Genet Article Genome-wide association studies (GWAS) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. Here we present an open resource that provides systematic fine-mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine-mapped genetics and functional genomics data using 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (OR = 8.1, 95% CI: (5.7, 11.5)). These results are publicly available through a web portal (http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets. 2021-11-01 2021-10-28 /pmc/articles/PMC7611956/ /pubmed/34711957 http://dx.doi.org/10.1038/s41588-021-00945-5 Text en https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms
spellingShingle Article
Mountjoy, Edward
Schmidt, Ellen M.
Carmona, Miguel
Schwartzentruber, Jeremy
Peat, Gareth
Miranda, Alfredo
Fumis, Luca
Hayhurst, James
Buniello, Annalisa
Karim, Mohd Anisul
Wright, Daniel
Hercules, Andrew
Papa, Eliseo
Fauman, Eric B.
Barrett, Jeffrey C.
Todd, John A.
Ochoa, David
Dunham, Ian
Ghoussaini, Maya
An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci
title An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci
title_full An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci
title_fullStr An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci
title_full_unstemmed An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci
title_short An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci
title_sort open approach to systematically prioritize causal variants and genes at all published human gwas trait-associated loci
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611956/
https://www.ncbi.nlm.nih.gov/pubmed/34711957
http://dx.doi.org/10.1038/s41588-021-00945-5
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