Evidence map›Paper›PMID 38045390›Full record

ArticleResearch square2023

Epistasis regulates genetic control of cardiac hypertrophy.

Qianru Wang, Tiffany M Tang, Nathan Youlton, Chad S Weldy, Ana M Kenney, Omer Ronen, J Weston Hughes, Elizabeth T Chin, Shirley C Sutton, Abhineet Agarwal and 14 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors at 8 institutions in 2 countries.

Qianru WangDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-7219-7522
Tiffany M TangDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
Nathan YoultonDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Chad S WeldyDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Ana M KenneyDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
Omer RonenDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
J Weston HughesDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Elizabeth T ChinDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Shirley C SuttonDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Abhineet AgarwalDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
Xiao LiDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
Merle BehrFaculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Karl KumbierDepartment of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
Christine S MoravecDepartment of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
W H Wilson TangDepartment of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.ORCID https://orcid.org/0000-0002-8335-735X
Kenneth B MarguliesDivision of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Thomas P CappolaDivision of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0002-7433-2740
Rima ArnaoutBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID https://orcid.org/0000-0002-7134-0040
James B BrownDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
James R PriestDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0002-8349-4784
Victoria N ParikhDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Bin YuDepartment of Statistics, University of California, Berkeley, Berkeley, CA, USA.
Euan A AshleyDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.ORCID https://orcid.org/0000-0001-9418-9577
Stanford University · USUniversity of California, Berkeley · USUniversity of California, San Francisco · USCalifornia University of Pennsylvania · USCleveland Clinic · USJohns Hopkins University · USLawrence Berkeley National Laboratory · USUniversity of Regensburg · DE

Funding

MTSS1 in Myocardial DiseaseR01HL141232 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI THOMAS P. CAPPOLA, Scott Michael Damrauer · 2019 to 2026
$4.7M
Structure function relationships from deep mutational scanning in human cardiomyopathyR01HL144843 · NHLBI · STANFORD UNIVERSITY · PI ASHLEY, EUAN A · 2020 to 2023
$2.8M
The Role of RBM20 Sequence and Expression in Dilated CardiomyopathiesK08HL143185 · NHLBI · STANFORD UNIVERSITY · PI PARIKH, VICTORIA · 2019 to 2023
$725k
A transcriptional network which governs smooth muscle transition is mediated by causal coronary artery disease gene PDGFDF32HL160067 · NHLBI · STANFORD UNIVERSITY · PI WELDY, CHAD S · 2021 to 2022
$135k
NHLBI NIH HHS F32 HL160067NHLBI NIH HHS K08 HL143185NHLBI NIH HHS L30 HL159413NHLBI NIH HHS R01 HL141232NHLBI NIH HHS R01 HL144843
6 · The paper itself

Abstract

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to

Identifiers

PMID38045390
PMCPMC10690313
OpenAlexW4388813285

What Socratic holds

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LicenceCC BY
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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 Socratic graph.