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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...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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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. |
format | Online Article Text |
id | pubmed-7611956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
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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