Evidence map›Paper›PMID 38977852›Full record

ArticleNature genetics2024

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

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

Abstract read
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In one paragraph

Article in Nature genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.

0numbers the graph read from it
0cells of the map it votes in
73citing 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

73 citing papers in PubMed.

  1. Article
  2. Article
  3. Genetic architectures of brain-related traits are shaped by strong selective constraints.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  12. Review
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  18. A sibling study of variation in parental mutation rates.bioRxiv : the preprint server for biology · 2026
    Article
  19. Article
  20. Article

13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Tony Zeng *Department of Genetics, Stanford University, Stanford, CA, USA. tkzeng@stanford.edu.ORCID http://orcid.org/0000-0002-6509-9879
Jeffrey P Spence *Department of Genetics, Stanford University, Stanford, CA, USA. jspence@stanford.edu.ORCID http://orcid.org/0000-0002-3199-1447
Hakhamanesh MostafaviDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-1060-2844
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford, CA, USA. pritch@stanford.edu.ORCID http://orcid.org/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
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 HG009431
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 constraints for the shortest ~25% of genes, potentially causing important pathogenic mutations to be overlooked. Here we developed a framework combining a population genetics model with machine learning on gene features to enable accurate inference of an interpretable constraint metric, s

Indexed as

Bayes TheoremEvolution, MolecularGenetics, PopulationModels, GeneticHumansMachine LearningMutationPhenotypeSelection, Genetic

Identifiers

What OpenQuestion holds

Textmetadata
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.