Evidence map›Paper›PMID 41573830›Full record

ArticlebioRxiv : the preprint server for biology2025

Allele Frequencies at Recessive Disease Genes are Mainly Determined by Pleiotropic Effects in Heterozygotes.

Jonathan Judd, Jeffrey P Spence, Nikhil Milind, Linda Kachuri, John S Witte, Jonathan K Pritchard

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

6 authors.

Jonathan JuddDepartment of Genetics, Stanford University, Stanford, CA.ORCID 0000-0001-8214-4849
Jeffrey P SpenceDepartment of Genetics, Stanford University, Stanford, CA.ORCID 0000-0002-3199-1447
Nikhil MilindDepartment of Genetics, Stanford University, Stanford, CA.ORCID 0000-0002-7975-247X
Linda KachuriDepartment of Epidemiology and Population Health, Stanford University, Stanford, CA.ORCID 0000-0002-3226-4727
John S WitteDepartment of Genetics, Stanford University, Stanford, CA.ORCID 0000-0003-0146-1434
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
Bayesian estimation of gene effects on traits from coding variantsR01HG014005 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2025 to 2026
$1.3M
NHGRI NIH HHS R01 HG008140NHGRI NIH HHS R01 HG014005
6 · The paper itself

Abstract

The classic theory of mutation-selection balance predicts the equilibrium frequency of genetic variation under negative selection. The model predicts a simple relationship between the total frequency of deleterious variants, mutation rate, and strength of selection, with different functions for recessive and (co-)dominant genes. In this study, we investigate whether genes associated with human recessive disorders fit the predictions of this classic model. By comparing observed frequencies of loss of function variants (LoFs) to those expected under mutation-selection balance we find that, for nearly all recessive genes, the observed frequencies are too low to be explained by purely recessive selection. Analyzing the effects of heterozygous LoFs on quantitative traits from the UK Biobank, we find that recessive disease genes have widespread quantitative effects in heterozygotes. Together, these results suggest that most selection experienced by pathogenic mutations in recessive disease genes may be due to stabilizing selection in heterozygotes. We conclude that very few human genes follow the classic model of recessive mutation-selection balance.

Identifiers

PMID41573830
PMCPMC12822701

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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.