Evidence map›Paper›PMID 37205493›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Rare penetrant mutations confer severe risk of common diseases.

Petko Fiziev, Jeremy McRae, Jacob C Ulirsch, Jacqueline S Dron, Tobias Hamp, Yanshen Yang, Pierrick Wainschtein, Zijian Ni, Joshua G Schraiber, Hong Gao and 11 more

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors at 5 institutions in 2 countries.

Petko FizievArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Jeremy McRaeArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Jacob C UlirschArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Jacqueline S DronCenter for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
Tobias HampArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Yanshen YangArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Pierrick WainschteinArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Zijian NiDepartment of Statistics, UW Madison; Madison, Wisconsin 53706, USA.
Joshua G SchraiberArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Hong GaoArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Dylan CableDepartment of Electrical Engineering and Computer Science, MIT; Cambridge, Massachusetts 02142, USA.
Yair FieldArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Francois AguetArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Marc FasnachtArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Ahmed MetwallyArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Jeffrey RogersHuman Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine; Houston, Texas 77030, USA.
Tomas Marques-BonetInstitute of Evolutionary Biology (UPF-CSIC); 08003 Barcelona, Spain.
Heidi L RehmCenter for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
Anne O'Donnell-LuriaProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, Massachusetts 02142, USA.
Amit V KheraCenter for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
Kyle Kai-How FarhArtificial Intelligence Laboratory, Illumina, Inc.; San Diego, California 92122, USA.
Illumina (United States) · USBroad Institute · USUniversity of Wisconsin–Madison · USDimagi (United States) · USInstitució Catalana de Recerca i Estudis Avançats · ES

Funding

Identification of Genetic and Molecular Bases of Derived Phenotypes in Primate Brain DevelopmentR01HG010898 · NHGRI · YALE UNIVERSITY · PI SANTPERE BARO, GABRIEL, SESTAN, NENAD · 2020 to 2023
$2.2M
NHGRI NIH HHS R01 HG010898
6 · The paper itself

Abstract

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction. One sentence summary: Rare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.

Identifiers

PMID37205493
PMCPMC10187340
OpenAlexW4367845628

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.