Evidence map›Paper›PMID 41824490›Full record

ArticlePLoS genetics2026

Wanted: A population genetic theory of biological noise regulation.

Daniel M Weinreich, Tom Sgouros, Yevgeniy Raynes, Hlib Burtsev, Edison Chang, Sanyu Rajakumar, Ignacio G Bravo, Csenge Petak

Abstract read
In one paragraph

Article in PLoS genetics, 2026. 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. Evolutionary dynamics under phenotypic uncertainty.bioRxiv : the preprint server for biology · 2026
    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

8 authors.

Daniel M WeinreichDepartment of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island United States of America.ORCID https://orcid.org/0000-0003-1424-7541
Tom SgourosData Science Institute, Brown University, Providence, Rhode Island United States of America.ORCID https://orcid.org/0000-0003-0939-6067
Yevgeniy RaynesDepartment of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island United States of America.ORCID https://orcid.org/0000-0003-1608-9479
Hlib BurtsevDepartment of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island United States of America.ORCID https://orcid.org/0009-0008-6384-8064
Edison ChangDepartment of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island United States of America.
Sanyu RajakumarDepartment of Ecology, Evolution and Organismal Biology, Brown University, Providence, Rhode Island United States of America.ORCID https://orcid.org/0009-0001-8225-0788
Ignacio G BravoLaboratory MIVEGEC (Univ Montpellier, CNRS, IRD) Centre National de la Recherche Scientifique, Montpellier France.ORCID https://orcid.org/0000-0003-3389-3389
Csenge PetakDepartment of Biology, University of Vermont, Burlington, Vermont United States of America.ORCID https://orcid.org/0000-0003-2715-8759

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Classical population genetics provides a robust, quantitative framework for modeling how natural selection acts on alleles that influence phenotypes with invariant fitness consequences for their carriers, such as running speed or drug resistance. By contrast, modifier theory considers the evolution of alleles that influence population genetic parameter values in their carriers, such as mutation or recombination rates. This is a more complicated problem. First, the fitness effects of modifier alleles reflect independently realized stochastic phenotype perturbations they induce in their carriers. And second, the association between modifier alleles and their induced phenotypes can decay over generations. Consequently, general results in modifier theory have been few. Here, we propose recasting modifier theory as exploring the evolution of alleles that influence the amount of stochasticity in inheritance, be it genetic, epigenetic, cytoplasmic or somatic transmission. We then present a toy model that predicts the existence of a selectively optimal amount of such "reproductive noise," which depends on the rate of environment change, the timescale of association between noise allele and induced phenotype, and population size. Next, we suggest that the same framework can be applied to the evolution of alleles that influence "developmental noise," i.e., the amount of stochastic phenotypic variation among genetically identical organisms reared in identical environments. This theoretical connection is timely, because high throughput assays are now demonstrating widespread heritability in the amount of developmental noise. Our approach also resolves the long-standing teleological criticism of the hypothesis that evolvability can evolve by natural selection. Taken together, this work demonstrates the opportunities for a robust, quantitative population genetic theory of alleles that influence the amount of biological noise.

Indexed as

Genetics, PopulationModels, GeneticSelection, GeneticAllelesAnimalsEvolution, MolecularGenetic FitnessMutationPhenotypeStochastic Processes

Identifiers

PMID41824490
PMCPMC13002102

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.