Evidence map›Paper›PMID 37292653›Full record

ArticlebioRxiv : the preprint server for biology2024

Bayesian estimation of gene constraint from an evolutionary model with gene features.

Tony Zeng, Jeffrey P Spence, Hakhamanesh Mostafavi, Jonathan K Pritchard

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Tony ZengDepartment of Genetics, Stanford University, Stanford CA.ORCID 0000-0002-6509-9879
Jeffrey P SpenceDepartment of Genetics, Stanford University, Stanford CA.ORCID 0000-0002-3199-1447
Hakhamanesh MostafaviDepartment of Genetics, Stanford University, Stanford CA.ORCID 0000-0002-1060-2844
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford CA.ORCID 0000-0002-8828-5236

Funding

Integration of genetic association mapping and functional data to elucidate genetic mechanisms of diseaseR01HG008140 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2016 to 2026
$7.3M
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
New methods for constructing and evaluating polygenic scoresR01HG011432 · NHGRI · STANFORD UNIVERSITY · PI PRITCHARD, JONATHAN K · 2020 to 2023
$3.3M
NHGRI NIH HHS R01 HG008140NHGRI NIH HHS R01 HG011432NHGRI NIH HHS U01 HG009431NIA NIH HHS R01 AG066490
6 · The paper itself

Abstract

Measures of selective constraint on genes have been used for many applications including clinical interpretation of rare coding variants, disease gene discovery, and studies of genome evolution. However, widely-used metrics are severely underpowered at detecting constraint for the shortest ∼25% of genes, potentially causing important pathogenic mutations to be overlooked. We developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric,

Identifiers

PMID37292653
PMCPMC10245655

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.