Evidence map›Paper›PMID 40890511›Full record

ReviewNature reviews. Cardiology2026

Creating an atlas of variant effects to resolve variants of uncertain significance and guide cardiovascular medicine.

Andrew M Glazer, Daniel R Tabet, Victoria N Parikh, Brett M Kroncke, Atina G Cote, Yuta Yamamoto, Qianru Wang, Ayesha Muhammad, Megan C Lancaster, Matthew J O'Neill and 6 more

Abstract readReview
In one paragraph

Review in Nature reviews. Cardiology, 2026. 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. Article
  3. Mapping the Functional Landscape ofmedRxiv : the preprint server for health sciences · 2025
    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

16 authors.

Andrew M GlazerDepartment of Medicine (Genetic Medicine and Clinical Pharmacology), Vanderbilt University Medical Center, Nashville, TN, USA.
Daniel R TabetDonnelly Centre; Department of Molecular Genetics; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Victoria N ParikhDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Brett M KronckeDepartment of Medicine (Genetic Medicine and Clinical Pharmacology), Vanderbilt University Medical Center, Nashville, TN, USA.
Atina G CoteDonnelly Centre; Department of Molecular Genetics; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Yuta YamamotoDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-6129-9745
Qianru WangDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-7219-7522
Ayesha MuhammadDepartment of Medicine (Genetic Medicine and Clinical Pharmacology), Vanderbilt University Medical Center, Nashville, TN, USA.
Megan C LancasterDepartment of Medicine (Cardiovascular Medicine), The Ohio State University, Columbus, OH, USA.ORCID 0000-0002-2697-3762
Matthew J O'NeillDepartment of Medicine (Genetic Medicine and Clinical Pharmacology), Vanderbilt University Medical Center, Nashville, TN, USA.
Jochen WeileDonnelly Centre; Department of Molecular Genetics; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Tao YangDepartment of Medicine (Genetic Medicine and Clinical Pharmacology), Vanderbilt University Medical Center, Nashville, TN, USA.
Calum A MacraeBrigham and Women's Hospital, Boston, MA, USA.ORCID 0000-0001-5181-2664
Euan A AshleyDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-9418-9577
Frederick P RothDonnelly Centre; Department of Molecular Genetics; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Dan M RodenDepartment of Medicine (Genetic Medicine and Clinical Pharmacology), Vanderbilt University Medical Center, Nashville, TN, USA. dan.roden@vumc.org.ORCID 0000-0002-6302-0389

Funding

Technology to understand genetic variant effects in contextRM1HG010461 · NHGRI · UNIVERSITY OF WASHINGTON · PI Douglas M Fowler, Bruce Colston Trapnell · 2019 to 2026
$18.9M
Molecular phenotyping of ~100,000 coding variants across Mendelian disease genesUM1HG011989 · NHGRI · DANA-FARBER CANCER INST · PI Marc Vidal · 2021 to 2026
$9.9M
Systematically mapping variant effects for cardiovascular genesR01HL164675 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI ASHLEY, EUAN A, RODEN, DAN M · 2022 to 2025
$8.1M
Pathogenic hotspots illuminate mechanism and therapeutic potential in arrhythmogenic cardiomyopathyR01HL168059 · NHLBI · STANFORD UNIVERSITY · PI Victoria Parikh · 2023 to 2026
$3.0M
Functional Genomics of Cardiac Sodium Channel VariantsR01HL149826 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI RODEN, DAN M · 2020 to 2023
$2.9M
Integrating KCNH2 Variant-Specific Features and Heterozygote Phenotypes to Estimate Long QT PenetranceR01HL160863 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Brett M Kroncke · 2022 to 2026
$2.9M
High-throughput discovery of disease-associated ion channel variantsR35GM150465 · NIGMS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Andrew M. Glazer · 2023 to 2026
$1.8M
A pipeline for identifying disease-causing variants in transmembrane proteinsR00HG010904 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, ANDREW M. · 2022 to 2024
$737k
A high-throughput, multimodal strategy to improve variant classificationK08HG014001 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Megan Lancaster · 2025 to 2026
$347k
A pipeline for identifying disease-causing variants in transmembrane proteinsK99HG010904 · NHGRI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI GLAZER, ANDREW M. · 2020 to 2021
$223k
NHGRI NIH HHS K08 HG014001NHGRI NIH HHS K99 HG010904NHGRI NIH HHS R00 HG010904NHGRI NIH HHS RM1 HG010461NHGRI NIH HHS UM1 HG011989NHLBI NIH HHS R01 HL149826NHLBI NIH HHS R01 HL160863NHLBI NIH HHS R01 HL164675NHLBI NIH HHS R01 HL168059NIGMS NIH HHS R35 GM150465
6 · The paper itself

Abstract

Cardiovascular diseases are leading global causes of death and disability, often presenting as interrelated phenotypes of atherosclerotic vascular disease, heart failure and arrhythmias. Cardiovascular diseases arise from interactions between environmental factors and predisposing genotypes and include common Mendelian lipid disorders, cardiomyopathies and arrhythmia syndromes. The identification of a pathogenic variant through genetic testing can inform disease diagnosis, risk prediction, treatment and family screening. However, a major roadblock in genomic medicine is that for many variants, especially missense variants, we lack sufficient evidence to enable a definitive classification, and therefore these variants are deemed as 'variants of uncertain significance'. In this Review, we describe how multiplexed assays of variant effects can enable the functional assessment of nearly all coding variants in a target sequence, potentially offering a proactive approach to identifying the functional significance of gene variants that are observed later in a patient. We discuss validation, including the role of in silico variant effect predictors, and how multiplexed experimental methods are informing cardiovascular disease biology and ultimately resolving the problem of variants of uncertain significance at scale.

Indexed as

Cardiovascular DiseasesGenetic TestingGenetic VariationGenetic Predisposition to DiseaseGenomicsHumansPhenotype

Identifiers

PMID40890511
PMCPMC12434709

What OpenQuestion holds

Textmetadata
LicenceTDM
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.