Evidence map›Paper›PMID 34582792›Full record

ArticleAmerican journal of human genetics2021

An integrated approach to identify environmental modulators of genetic risk factors for complex traits.

Brunilda Balliu, Ivan Carcamo-Orive, Michael J Gloudemans, Daniel C Nachun, Matthew G Durrant, Steven Gazal, Chong Y Park, David A Knowles, Martin Wabitsch, Thomas Quertermous and 2 more

Abstract read
In one paragraph

Article in American journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
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  6. Tradeoffs in Modeling Context Dependency in Complex Trait Genetics.bioRxiv : the preprint server for biology · 2025
    Article
  7. Review
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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.

Brunilda BalliuDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: bballiu@ucla.edu.
Ivan Carcamo-OriveDepartment of Medicine, Division of Cardiovascular Medicine, Cardiovascular Institute and Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA.
Michael J GloudemansBiomedical Informatics Training Program and Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Daniel C NachunDepartment of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Matthew G DurrantDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
Steven GazalCenter for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Chong Y ParkDepartment of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
David A KnowlesNew York Genome Center, New York, NY 10013, USA; Department of Computer Science, Columbia University, New York, NY 10027, USA.
Martin WabitschDepartment of Pediatrics and Adolescent Medicine, Division of Pediatric Endocrinology, Ulm University, Ulm 89075, Germany.
Thomas QuertermousDepartment of Medicine, Division of Cardiology and Cardiovascular Institute, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA.
Joshua W KnowlesDepartment of Medicine, Division of Cardiology and Cardiovascular Institute, Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: knowlej@stanford.edu.
Stephen B MontgomeryDepartment of Pathology and Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Causal variant association mechanisms in TCF21 binding coronary disease lociR01HL134817 · NHLBI · STANFORD UNIVERSITY · PI THOMAS QUERTERMOUS · 2017 to 2026
$6.2M
Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
The SMAD3 signaling network in coronary artery disease riskR01HL139478 · NHLBI · STANFORD UNIVERSITY · PI THOMAS QUERTERMOUS · 2018 to 2026
$3.7M
Mapping Molecular and Phenotypic Interactions in Alzheimers DiseaseR01AG066490 · NIA · STANFORD UNIVERSITY · PI MONTGOMERY, STEPHEN · 2020 to 2024
$3.6M
Decoding the regulatory architecture of the human genome across cell types, individuals and diseaseU01HG009431 · NHGRI · STANFORD UNIVERSITY · PI PRITCHARD, JONATHAN K · 2017 to 2021
$3.5M
Characterization of novel insulin resistance genes by gene editing, high-throughput phenotyping and in vivo studiesR01DK120565 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2019 to 2023
$3.2M
PDGFD regulates a transcriptional network to modulate smooth muscle cell transition and coronary artery disease riskR01HL156846 · NHLBI · STANFORD UNIVERSITY · PI QUERTERMOUS, THOMAS · 2021 to 2024
$2.7M
Identifying tobacco-genetic interactions through study of the aryl hydrocarbon receptor pathway.R01HL151535 · NHLBI · STANFORD UNIVERSITY · PI QUERTERMOUS, THOMAS · 2021 to 2024
$2.7M
Mechanisms of NAT2 Regulation of Insulin Resistance and Mitochondrial DysfunctionR01DK116750 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2019 to 2023
$2.4M
Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to functionR01DK106236 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2016 to 2020
$2.4M
Molecular Mechanisms of Insulin Resistance Associated LociR01DK107437 · NIDDK · STANFORD UNIVERSITY · PI QUERTERMOUS, THOMAS · 2016 to 2019
$2.2M
NHGRI NIH HHS R01 HG008150NHGRI NIH HHS U01 HG009080NHGRI NIH HHS U01 HG009431NHLBI NIH HHS R01 HL134817NHLBI NIH HHS R01 HL139478NHLBI NIH HHS R01 HL142015NHLBI NIH HHS R01 HL145708NHLBI NIH HHS R01 HL151535NHLBI NIH HHS R01 HL156846NIA NIH HHS R01 AG066490NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R01 DK106236NIDDK NIH HHS R01 DK107437NIDDK NIH HHS R01 DK116750NIDDK NIH HHS R01 DK120565
6 · The paper itself

Abstract

Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal-muscle-, fat-, and liver-relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWASs from 31 distinct polygenic traits, we show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS Catalog. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.

Indexed as

Gene-Environment InteractionGenetic Predisposition to DiseaseGenome-Wide Association StudyMultifactorial InheritancePolymorphism, Single NucleotideQuantitative Trait LociAutoimmune DiseasesHumansMental DisordersMetabolic DiseasesPhenotypecolocalizationeQTLgene-by-environment interactionsgene expressionGWAS

Identifiers

PMID34582792
PMCPMC8546041

What OpenQuestion holds

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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 OpenQuestion graph.