Protocol to estimate the heritability of drug response with GxEMM and identify gene-drug interactions with TxEWAS.

Michal Sadowski, Andy W Dahl, Noah Zaitlen

Abstract read

In one paragraph

Article in STAR protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

The geometry of G × E: How scaling and endogenous treatment effects shape interaction direction.PLoS genetics · 2026
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

3 authors.

Michal SadowskiBioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: michalsadowski@ucla.edu.

Andy W DahlSection of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA.

Noah ZaitlenBioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Funding

Methods for Genome-wide Association Studies in Admixed PopulationsR01HG006399 · NHGRI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI PRICE, ALKES L · 2011 to 2024

$6.3M

Novel statistical genetics methods to unravel polygenic interactions in complex traitsR35GM150822 · NIGMS · UNIVERSITY OF CHICAGO · PI Andrew Dahl · 2023 to 2026

$1.6M

NHGRI NIH HHS R01 HG006399NIGMS NIH HHS R35 GM150822

6 · The paper itself

Abstract

Identifying factors that affect treatment response is a central objective of clinical research. Here, we present a protocol to study the genetic architecture of response to commonly prescribed drugs using gene-context interaction techniques. We describe steps for estimating the heritability of drug response with gene-environment interaction mixed model (GxEMM) and identifying gene-drug interactions with gene-environment interaction transcriptome-wide association study (TxEWAS). While the protocol describes application to drug treatments, this framework can be used to characterize the genetic basis of any covariate's effect. For complete details on the use and execution of this protocol, please refer to Sadowski et al.

Indexed as

Gene-Environment InteractionGenome-Wide Association StudyTranscriptomeHumansModels, GeneticBioinformaticsEnvironmental sciencesGene ExpressionGeneticsGenomicsHealth Sciences

Identifiers

PMID40249708

DOI10.1016/j.xpro.2025.103780

PMCPMC12224878

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

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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 Socratic graph.