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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Data availability
Our results are freely available through a web portal (genetics.opentargets.org), GraphQL API or through bulk download. GWAS gold-standard genes: github.com/opentargets/genetics-gold-standards.
Code availability
All analysis code is available open source (Apache license) in the following repositories:
https://github.com/opentargets/genetics-sumstat-data
https://github.com/opentargets/genetics-finemapping
https://github.com/opentargets/genetics-colocalisation
https://github.com/opentargets/genetics-v2d-data
https://github.com/opentargets/genetics-v2g-data
https://github.com/opentargets/genetics-l2g-scoring
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Acknowledgements
We thank E. McDonagh, J. Maranville and D. Hulcoop for their useful feedback to improve the paper, and H. Parkinson, J. MacArthur, D. Zerbino and K. Alasoo for their support with the GWAS Catalog and eQTL Catalogue data. This research has been conducted using the UK Biobank Resource. This work was funded by Open Targets. E.M. was funded by JDRF (4-SRA-2017-473-A-N) to the Diabetes and Inflammation Laboratory, University of Oxford. This research was funded in part by a Wellcome Trust grant (no. 206194). For the purpose of Open Access, the authors have applied a CC-BY public copyright license to any author-accepted manuscript version arising from this submission.
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Contributions
M.G., J.S., E.M. and I.D. wrote the manuscript. E.M. conducted the analysis and designed and built the machine-learning model. E.M., E.M.S. and M.G. prioritized GWASs for curation from the GWAS Catalog. E.M., M.C., A.B., J.H. and E.P. curated and processed the GWASs and functional genomics data. E.B.F., E.M. and M.G. curated the gold standards. G.P., A.M., L.F., A.H., E.P. and M.C. designed and implemented visualizations for analysis. J.S. conducted fine-mapping comparisons and Mendelian disease enrichments. D.O. performed additional analysis. I.D., M.G., J.A.T. and J.C.B. conceived and supervised the study. M.A.K. generated Fig. 1. M.G., E.M., E.M.S., D.W. and E.P. worked on the biological questions and the underlying visualizations in the portal.
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J.A.T. is a member of the GSK Human Genetics Advisory Board. E.B.F. is a full-time employee of and shareholder in Pfizer, Inc. E.P. was an employee of Biogen at the time of the work. E.P. is now an employee of AstraZeneca. The remaining authors do not have any competing interests.
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Peer review information Nature Genetics thanks Guillaume Lettre and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.
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Extended data
Extended Data Fig. 1 Difference between fine-mapping methods.
a, Histogram of the absolute difference in the number of variants in the 95% credible set across all loci. The median absolute difference was 7 variants. b, Histogram of the posterior probability at a given locus that is contained in variants shared between the 95% credible sets of the two methods, determined for all loci. The median shared variant probability was 0.70.
Extended Data Fig. 2 Minor allele frequency of the lead variant in 1000 Genomes European samples, stratified by the number of variants in the 95% credible set.
Association signals with smaller credible sets tend to be from lower frequency haplotypes. This effect is especially pronounced for the lowest credible set size bins (1 and 2-5).
Extended Data Fig. 3 Schematic showing how variant to gene distance features are calculated.
Distance to the transcription start site (TSS) is the number of bases from the variant to the TSS of the canonical transcript of the gene as defined by Ensembl. Distance to the gene footprint is the smallest number of base pairs between the variant and any position between the TSS and transcription end site of the canonical transcript. (Calculations, left) Some L2G distance features are an average across variants, weighted by each variant’s posterior probability from fine mapping. (Calculations, right) ‘Neighborhood’ features are defined on a log scale relative to the gene with the best score in that category (here, smallest distance). The negative log is used so that genes with better feature values have higher neighborhood scores.
Extended Data Fig. 4 Missingness.
The fraction of variants with missing values (no annotation in that category) is shown for representative input features of the L2G model.
Extended Data Fig. 5 Histogram of the number of prioritized genes (having L2G score ≥ 0.5) at each locus.
Very few loci have more than one gene prioritized.
Extended Data Fig. 6 Mendelian disease genes with matching phenotypes at GWAS loci are enriched for having high L2G scores.
For nine GWAS traits, we show the distribution of L2G scores for all Mendelian disease genes within 100 kb of a GWAS signal, stratified by whether or not the Mendelian disease has a matching phenotype term with the GWAS trait.
Extended Data Fig. 7 Gene enrichment.
Enrichment of genes with model score ≥ 0.5 for the distance-only models (top 2) or the full L2G model (bottom), stratified by whether the gene is a known drug target in ChEMBL phase ≥ 2, ≥ 3, or ≥ 4. Error bars show the 95% confidence interval from a two-sided Fisher’s exact test. The total numbers of positive gene-indication pairs used were 329, 216 and 159 for phases ≥2, ≥3 and ≥4 respectively. The overall sample size, including positive and negative gene-indication pairs, was 104,934.
Extended Data Fig. 8 Feature distributions.
Each plot shows the mean value of a given predictor across different gold-standard datasets (y-axis) for either gold standard positive genes (GSP, green) or gold standard negative genes (GSN, yellow). GSP genes are more easily distinguished from GSN genes by distance in the manually curated datasets (especially Progem, Fauman_twitter, and T2D).
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Mountjoy, E., Schmidt, E.M., Carmona, M. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat Genet 53, 1527–1533 (2021). https://doi.org/10.1038/s41588-021-00945-5
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DOI: https://doi.org/10.1038/s41588-021-00945-5
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