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Pividori M, Rajagopal PS, Barbeira A, et al. PhenomeXcan: Mapping the genome to the phenome through the transcriptome. Sci Adv. 2020;6(37)doi: 10.1126/sciadv.aba2083.
Pividori, M., Rajagopal, P. S., Barbeira, A., Liang, Y., Melia, O., Bastarache, L., Park, Y., Consortium, G., Wen, X., & Im, H. K. (2020). PhenomeXcan: Mapping the genome to the phenome through the transcriptome. Science advances, 6(37), . https://doi.org/10.1126/sciadv.aba2083
Pividori, Milton, et al. "PhenomeXcan: Mapping the genome to the phenome through the transcriptome." Science advances vol. 6,37 (2020). doi: https://doi.org/10.1126/sciadv.aba2083
Pividori M, Rajagopal PS, Barbeira A, Liang Y, Melia O, Bastarache L, Park Y, Consortium G, Wen X, Im HK. PhenomeXcan: Mapping the genome to the phenome through the transcriptome. Sci Adv. 2020 Sep 10;6(37). doi: 10.1126/sciadv.aba2083. Print 2020 Sep. PMID: 32917697.
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Sci Adv. 2020 Sep 10;6(37). doi: 10.1126/sciadv.aba2083. Print 2020 Sep.

PhenomeXcan: Mapping the genome to the phenome through the transcriptome.

Science advances

Milton Pividori, Padma S Rajagopal, Alvaro Barbeira, Yanyu Liang, Owen Melia, Lisa Bastarache, YoSon Park, GTEx Consortium, Xiaoquan Wen, Hae K Im

Affiliations

  1. Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.
  2. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
  3. Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL, USA.
  4. Department of Biomedical Informatics, Department of Medicine, Vanderbilt University, Nashville, TN, USA.
  5. Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, USA.
  6. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA. [email protected] [email protected].
  7. Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA. [email protected] [email protected].

PMID: 32917697 DOI: 10.1126/sciadv.aba2083

Abstract

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.

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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