Genetically supported drug target prioritization for rare diseases

Chen, Robert, Duffy, Áine, Mort, Matthew, Cooper, David N. ORCID: https://orcid.org/0000-0002-8943-8484, Rocheleau, Ghislain, Jordan, Daniel M. and Do, Ron 2026. Genetically supported drug target prioritization for rare diseases. Genome Medicine 18 (1) , 83. 10.1186/s13073-026-01671-5

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Abstract

RareGPS is a machine-learning framework prioritizing drug targets for rare and uncommon diseases, integrating 11 genetic, clinical, and experimental evidence sources. It uses the full distribution of genetic associations across allele-frequency bins in an allelic-series model. Across 161 phenotypes, RareGPS outperforms existing resources for predicting drug indications and clinical trial progression; top 1% targets show 58-fold higher likelihood of advancing from nonindicated to phase IV and 8-fold from phase I to IV versus the middle 50%. We validated RareGPS using prescriptome analyses in two million patients and an independent literature evaluation tool (AMELIE). We publish predictions for 3,021,965 gene-phenotype pairs.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc-nd/4.0/, Type: open-access
Publisher:	BioMed Central
Date of First Compliant Deposit:	15 June 2026
Date of Acceptance:	19 May 2026
Last Modified:	15 Jun 2026 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/187554

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