Evidence map›Paper›PMID 41027901›Full record

ArticleNature communications2025

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 and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Áine DuffyThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Robert ChenThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
David SteinThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0002-5131-8313
Joshua K ParkThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Matthew MortInstitute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK.
Marie VerbanckUniversité Paris Cité, UR 7537 BioSTM, Paris, France.ORCID http://orcid.org/0000-0002-1669-572X
Avner SchlessingerDepartment of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0003-4007-7814
Yuval ItanThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0003-4966-3238
David N CooperInstitute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, UK.ORCID http://orcid.org/0000-0002-8943-8484
Daniel M JordanThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Ghislain RocheleauThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0002-9989-7553
Ron DoThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ron.do@mssm.edu.ORCID http://orcid.org/0000-0002-3144-3627

Funding

Towards an integrated map of causal connections for common, complex diseasesR35GM124836 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Ron Do · 2017 to 2026
$3.9M
NIGMS NIH HHS R35 GM124836U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35-GM124836
6 · The paper itself

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.

Indexed as

Drug-Related Side Effects and Adverse ReactionsComputer SimulationHumans

Identifiers

PMID41027901
PMCPMC12484675

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.