PhenomeXcan: Mapping the genome to the phenome through the transcriptome

Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.

PhenomeXcan is publicly available at phenomexcan.org. The site contains the results of S-PrediXcan (individual tissues reported) and S-MultiXcan (across all tissues) applied to 4091 traits and 22,515 genes. PhenomeXcan can be queried by gene (to result in traits) or trait (to result in genes). Multiple genes or traits can be queried at once. The result will list associations by P value (from either S-PrediXcan if tissue-specific or S-MultiXcan as the best across tissues) and locus RCP from fastENLOC. We have also provided a queryable table of PhenomeXcan’s 4091 traits × 5094 ClinVar traits. Queries can be made by either PhenomeXcan trait or ClinVar trait, and the result will list associated traits, shared genes in the association, and mean Z score. The datasets used in this paper are publicly available in https://doi.org/10.5281/zenodo.3530669. Our GitHub for PhenomeXcan (https://github.com/hakyimlab/phenomexcan) contains the instructions to download summaries of the results, the complete set of raw results, and code/scripts to reproduce all analyses and figures. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.