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Cited by 581Genetic Associations and EpidemiologyBioinformatics and Genomic NetworksGenomics and Rare Diseases

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

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Mountjoy, Edward, Schmidt, Ellen M., Carmona, Miguel, Schwartzentruber, Jeremy, Peat, Gareth, Miranda, Alfredo, Fumis, Luca, Hayhurst, James +11 more·2021·Nature Genetics
DOI 10.1038/s41588-021-00945-5PMC PMC7611956
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Abstract

Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, 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 and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio = 8.1, 95% confidence interval = 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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  1. [2]Altshuler D Daly MJ Lander ES Genetic mapping in human disease Science 2008 322 881 888 18988837 PMC2694957
  2. [3]Claussnitzer M FTO obesity variant circuitry and adipocyte browning in humans N Engl J Med 2015 373 895 907 26287746 PMC4959911
  3. [4]Zhu Z Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets Nat Genet 2016 48 481 487 27019110
  4. [5]Brænne I Prediction of causal candidate genes in coronary artery disease loci Arterioscler Thromb Vasc Biol 2015 35 2207 2217 26293461 PMC4583353
  5. [6]Fachal L Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes Nat Genet 2020 52 56 73 31911677 PMC6974400
  6. [7]Xue A Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes Nat Commun 2018 9 2941 30054458 PMC6063971
  7. [8]Okada Y Genetics of rheumatoid arthritis contributes to biology and drug discovery Nature 2014 506 376 381 24390342 PMC3944098
  8. [9]Fang H A genetics-led approach defines the drug target landscape of 30 immune-related traits Nat Genet 2019 51 1082 1091 31253980 PMC7124888
  9. [10]Hay M Thomas DW Craighead JL Economides C Rosenthal J Clinical development success rates for investigational drugs Nat Biotechnol 2014 32 40 51 24406927
  10. [12]Carvalho-Silva D Open Targets Platform: new developments and updates two years on Nucleic Acids Res 2019 47 D1056 D1065 30462303 PMC6324073
  11. [13]Koscielny G Open Targets: a platform for therapeutic target identification and validation Nucleic Acids Res 2017 45 D985 D994 27899665 PMC5210543
  12. [14]Ochoa D Open Targets Platform: supporting systematic drug-target identification and prioritisation Nucleic Acids Res 2021 49 D1302 D1310 33196847 PMC7779013
  13. [15]GTEx Consortium Genetic effects on gene expression across human tissues Nature 2017 550 204 213 29022597 PMC5776756
  14. [17]Kerimov N eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs bioRxiv 10.1101/2020.01.29.924266 PMC8423625 34493866 · DOI
  15. [18]Sun BB Genomic atlas of the human plasma proteome Nature 2018 558 73 79 29875488 PMC6697541
  16. [19]Buniello A The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 Nucleic Acids Res 2019 47 D1005 D1012 30445434 PMC6323933
  17. [20]Zhou W Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies Nat Genet 2018 50 1335 1341 30104761 PMC6119127
  18. [23]Wellcome Trust Case Control Consortium Bayesian refinement of association signals for 14 loci in 3 common diseases Nat Genet 2012 44 1294 1301 23104008 PMC3791416
  19. [24]Farh KK-H Genetic and epigenetic fine mapping of causal autoimmune disease variants Nature 2015 518 337 343 25363779 PMC4336207
  20. [25]McLaren W The Ensembl Variant Effect Predictor Genome Biol 2016 17 122 27268795 PMC4893825
  21. [26]Michailidou K Association analysis identifies 65 new breast cancer risk loci Nature 2017 551 92 94 29059683 PMC5798588
  22. [27]Kawase T PH domain-only protein PHLDA3 is a p53-regulated repressor of Akt Cell 2009 136 535 550 19203586
  23. [30]Surendran S An update on vitamin B12-related gene polymorphisms and B12 status Genes Nutr 2018 13 2 29445423 PMC5801754
  24. [31]Todd JA Evidence that UBASH3 is a causal gene for type 1 diabetes Eur J Hum Genet 2018 26 925 927 29760431 PMC6018710
  25. [32]de Lange KM Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease Nat Genet 2017 49 256 261 28067908 PMC5289481
  26. [33]Mosnier J-F ADAM15 upregulation and interaction with multiple binding partners in inflammatory bowel disease Lab Invest 2006 86 1064 1073 16894352
  27. [36]Thurman RE The accessible chromatin landscape of the human genome Nature 2012 489 75 82 22955617 PMC3721348
  28. [38]Soskic B Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases Nat Genet 2019 51 1486 1493 31548716 PMC6872452
  29. [39]Bycroft C The UK Biobank resource with deep phenotyping and genomic data Nature 2018 562 203 209 30305743 PMC6786975
  30. [40]Wakefield J Bayes factors for genome-wide association studies: comparison with P-values Genet Epidemiol 2009 33 79 86 18642345
  31. [44]Zerbino DR Ensembl 2018 Nucleic Acids Res 2018 46 D754 D761 29155950 PMC5753206
  32. [45]Karczewski KJ The mutational constraint spectrum quantified from variation in 141,456 humans Nature 2020 581 434 443 32461654 PMC7334197
  33. [46]Stacey D ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci Nucleic Acids Res 2019 47 e3 30239796 PMC6326795
  34. [48]Gaulton A The ChEMBL database in 2017 Nucleic Acids Res 2017 45 D945 D954 27899562 PMC5210557
  35. [51]Pencina MJ D’Agostino RB D’Agostino RB Vasan RS Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond Stat Med 2008 27 157 172 17569110
  36. [52]Pencina MJ D’Agostino RB SrSteyerberg EW Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers Stat Med 2011 30 11 21 21204120 PMC3341973
  37. [53]Sobczyk MK Gaunt TR Paternoster L MendelVar: gene prioritisation at GWAS loci using phenotypic enrichment of mendelian disease genes Bioinformatics 2021 37 1 8 33836063 PMC8034535

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Authors
Mountjoy, E., Schmidt, E. M., Carmona, M., Schwartzentruber, J., Peat, G., Miranda, A., Fumis, L., Hayhurst, J., Buniello, A., Karim, M. A., Wright, D., Hercules, A., Papa, E., Fauman, E. B., Barrett, J. C., Todd, J. A., Ochoa, D., Dunham, I., & Ghoussaini, M.
Affiliations
  • Wellcome Sanger Institute (GB)
  • European Bioinformatics Institute (GB)
  • Biogen (United States) (US)
  • Pfizer (United States) (US)
  • Centre for Human Genetics (GB)
Corresponding author
Ghoussaini, Maya
ORCID
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2021-10-28
Volume
53
Issue
11
Pages
1527-1533
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
DOI
10.1038/s41588-021-00945-5
PMC
PMC7611956
OpenAlex
W3210601686
Cited by
581
References
14
Citations by year
2026: 91 · 2025: 149 · 2024: 159 · 2023: 112 · 2022: 64 · 2021: 6
FWCI
51.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Genetics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
Wellcome Trust (206194)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
causal variant prioritization, gene prioritization, fine mapping, GWAS loci, drug target identification, machine learning, open resources, complex traits
MeSH terms
Machine Learning, Chromosome Mapping, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Genomics, Quantitative Trait Loci, Genome-Wide Association Study, Epigenomics

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