Evidence map›Paper›PMID 39657681›Full record

ArticleCell reports methods2024

Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease.

Ben O Petrazzini, Daniel J Balick, Iain S Forrest, Judy Cho, Ghislain Rocheleau, Daniel M Jordan, Ron Do

Abstract read
In one paragraph

Article in Cell reports methods, 2024. 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. Article
  2. Article
  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

7 authors.

Ben O PetrazziniThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Daniel J BalickThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard, Medical School, Boston, MA, USA.
Iain S ForrestThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Judy ChoThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Ghislain RocheleauThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Daniel M JordanThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Ron DoThe Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: ron.do@mssm.edu.

Funding

MOUNT SINAI MEDICAL SCIENTIST TRAINING PROGRAMT32GM007280 · NIGMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI BARON, MARGARET H · 1985 to 2021
$22.8M
Institutional Career Development CoreKL2TR004421 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Inga Peter · 2022 to 2026
$6.2M
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
Elucidating hereditary transthyretin-mediated heart failure risk using machine learning, polygenic risk and recall by genotype approaches in African ancestry individualsR01HL155915 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DO, RON, NADKARNI, GIRISH NITIN · 2021 to 2024
$3.4M
Resolving Causal Influences Among Correlated Risk Biomarkers for Coronary Artery DiseaseR01HL139865 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DO, RON · 2018 to 2021
$1.7M
NCATS NIH HHS KL2 TR004421NHLBI NIH HHS R01 HL139865NHLBI NIH HHS R01 HL155915NIGMS NIH HHS R35 GM124836NIGMS NIH HHS T32 GM007280
6 · The paper itself

Abstract

Mode of inheritance (MOI) is necessary for clinical interpretation of pathogenic variants; however, the majority of variants lack this information. Furthermore, variant effect predictors are fundamentally insensitive to recessive-acting diseases. Here, we present MOI-Pred, a variant pathogenicity prediction tool that accounts for MOI, and ConMOI, a consensus method that integrates variant MOI predictions from three independent tools. MOI-Pred integrates evolutionary and functional annotations to produce variant-level predictions that are sensitive to both dominant-acting and recessive-acting pathogenic variants. Both MOI-Pred and ConMOI show state-of-the-art performance on standard benchmarks. Importantly, dominant and recessive predictions from both tools are enriched in individuals with pathogenic variants for dominant- and recessive-acting diseases, respectively, in a real-world electronic health record (EHR)-based validation approach of 29,981 individuals. ConMOI outperforms its component methods in benchmarking and validation, demonstrating the value of consensus among multiple prediction methods. Predictions for all possible missense variants are provided in the "Data and code availability" section.

Indexed as

Mutation, MissenseComputational BiologyConsensusDatabases, GeneticGenes, RecessiveGenetic Predisposition to DiseaseHumansSoftwareautosomal dominantautosomal recessiveCP: geneticsCP: systems biologyelectronic health recordsmode of inheritancepathogenic variantsvariant effect prediction

Identifiers

PMID39657681
PMCPMC11704621

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.