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

Abstract

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.

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

“…Examination of the chromosome 7 locus in published GWAS results using the Open Targets Genetics web portal ( 49 ) indicated smaller and nonsignificant effects on all psychiatric disorders ( Figure 1B ). Additionally, the SA-index SNP has been implicated at genome-wide significance in lifetime smoking index ( 50 ) (accounts for duration and amount of smoking) and propensity toward risk-taking behavior ( 51 ), although again with smaller effect sizes than on SA ( Figure 1B ; Tables S5 and S6 in Supplement 2 ).…”

Fine mapping with epigenetic information and 3D structure

“…Finally, meaningful integration of the vast wealth of functional data and knowledge that has been generated in the past few years is essential for elucidating biological networks that are altered in disease and for fulfilling the potential of GWAS for drug development and repositioning. Initiatives such as Open Targets [ 105 , 106 ] and bioinformatics pipelines like EpiMap [ 54 ], IMPACT [ 55 ] and others [ 45 , 107 , 108 ] provide frameworks to prioritize potential targets by integrating GWAS data with genomic features, disease ontologies and network connectivity. However, data integration still currently remains a challenge, and further research is needed in this area.…”

Genome-Wide Association Study-Guided Exome Rare Variant Burden Analysis Identifies IL1R1 and CD3E as Potential Autoimmunity Risk Genes for Celiac Disease

“…Several rare variant burden (RVB) analyses from large-scale WES have been successful in understanding the molecular basis of a broad range of complex diseases including epilepsy ( 63 ), autism ( 64 ), schizophrenia ( 65 ), and amyotrophic lateral sclerosis (ALS) ( 66 ). A recent study had proven the power of combining WES and GWAS on a wide level to study multiple complex diseases ( 67 ). Nonetheless, our study has more targeted findings, as we selected rare familial cases to benefit from the shared genetic composition in the affected siblings (nonetheless, since we selected rare familial cases to benefit from the shared genetic composition of the affected siblings, our study had more focused findings).…”