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

By Edward Mountjoy, Ellen M. Schmidt, M. Carmona, Gareth Peat, Alfredo Miranda, Luca Fumis, J. Hayhurst, A. Buniello, J. Schwartzentruber, M. Karim and 9 more. Nature Genetics, 2021

462 citations recorded by the source corpus.

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. Open Targets Genetics is a community resource that provides systematic fine mapping at human GWAS loci, enabling users to prioritize genes at disease-associated regions and assess their potential as drug targets.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3071fb0f590daf84253ff458c7b7922fd7564300

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Citations in the corpus

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020, methodology): “Noting previous work on the utility of combining locus-based and similarity-based gene prioritization methods 19,37 , we investigated agreement among methods, including genes with fine-mapped (PIP > 0.1) coding variants.”
The next-generation Open Targets Platform: reimagined, redesigned, rebuilt (2022, methodology): “To enhance the causal inference, the Genetics portal enriched the L2G machine learning (ML) regression with additional eQTLs from GTEx v8 and pQTLs from 6 new studies (19,20).”