Development of a genetic priority score to predict drug side effects using human genetic evidence

Áine Duffy, Robert Chen, David Stein, Joshua K. Park, Matthew Mort, Marie Verbanck, Avner Schlessinger, Yuval Itan, David N. Cooper, Daniel M. Jordan, Ghislain Rocheleau and Ron Do ()
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Áine Duffy: Icahn School of Medicine at Mount Sinai
Robert Chen: Icahn School of Medicine at Mount Sinai
David Stein: Icahn School of Medicine at Mount Sinai
Joshua K. Park: Icahn School of Medicine at Mount Sinai
Matthew Mort: Cardiff University
Marie Verbanck: UR 7537 BioSTM
Avner Schlessinger: Icahn School of Medicine at Mount Sinai
Yuval Itan: Icahn School of Medicine at Mount Sinai
David N. Cooper: Cardiff University
Daniel M. Jordan: Icahn School of Medicine at Mount Sinai
Ghislain Rocheleau: Icahn School of Medicine at Mount Sinai
Ron Do: Icahn School of Medicine at Mount Sinai

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Many drug failures in clinical trials are due to inadequate safety profiles. We developed an in-silico side effect genetic priority score (SE-GPS) that leverages human genetic evidence to inform side effect risk for a given drug target. We construct the SE-GPS in the Open Target dataset using post-marketing side effect data, externally test it in OnSIDES using side effects reported from drug labels and then generate a SE-GPS for 19,422 protein coding genes and 502 phecodes, of which 1.7% had a SE-GPS > 0. To consider drug mechanism, we incorporated the direction of genetic effect into a directional version of the score called the SE-GPS-DOE. We observe that restricting to at least two lines of genetic evidence conferred a 2.3- and 2.5-fold increased risk in side effects in Open Targets and OnSIDES respectively, with increased enrichments in severe drugs. We make all predictions publicly available in a web portal.

Date: 2025
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DOI: 10.1038/s41467-025-63762-y

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