Evidence map›Paper›PMID 42709909›Full record

ArticlePLoS biology2026

The distribution of fitness effects of nonsynonymous mutations varies phylogenetically across animals.

Meixi Lin, Sneha Chakraborty, Carlos Eduardo G Amorim, Sergio F Nigenda-Morales, Annabel C Beichman, Paulina G Nuñez-Valencia, Jonathan C Mah, Jacqueline A Robinson, Christopher C Kyriazis, Christian D Huber and 6 more

Abstract read
In one paragraph

Article in PLoS biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Meixi LinDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.ORCID https://orcid.org/0000-0001-5233-0675
Sneha ChakrabortyDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.
Carlos Eduardo G AmorimCenter for Evolution and Medicine, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America.
Sergio F Nigenda-MoralesAdvanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, Mexico.
Annabel C BeichmanDepartment of Genome Sciences, University of Washington, Seattle, Washington, United States of America.
Paulina G Nuñez-ValenciaAdvanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, Mexico.
Jonathan C MahBioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, California, United States of America.
Jacqueline A RobinsonDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.
Christopher C KyriazisDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.
Christian D HuberDepartment of Biology, Pennsylvania State University, University Park, Pennsylvania, United States of America.
Andrew E WebbDepartment of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America.
Sarah D KocherDepartment of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America.
Frederick I ArcherMarine Mammal and Turtle Division, Southwest Fisheries Science Center, La Jolla, California, United States of America.
Andrés Moreno-EstradaAdvanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, Mexico.
Robert K WayneDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.
Kirk E LohmuellerDepartment of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.ORCID https://orcid.org/0000-0002-3874-369X

Funding

Biological Mechanisms of Healthy Aging Training GrantT32AG066574 · NIA · UNIVERSITY OF WASHINGTON · PI David J. Marcinek, Jessica E Young · 2020 to 2026
$5.3M
Population genomics of the selective effects of new mutationsR35GM119856 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LOHMUELLER, KIRK · 2016 to 2025
$3.4M
Characterizing Human-Pathogen Interactions and Natural Selection with Ancient DNAR35GM142939 · NIGMS · CALIFORNIA STATE UNIVERSITY NORTHRIDGE · PI GUERRA AMORIM, CARLOS EDUARDO · 2021 to 2025
$1.8M
NIA NIH HHS T32 AG066574NIGMS NIH HHS R35 GM119856NIGMS NIH HHS R35 GM142939
6 · The paper itself

Abstract

The distribution of fitness effects (DFE) describes the selection coefficients of newly arising mutations and fundamentally influences population genetic processes. However, the extent and mechanisms of differences in the DFE for non-synonymous mutations have not been systematically investigated across species with divergent phylogenetic histories and ecologies. Here, we inferred the DFE in natural populations of 11 animal (sub)species, including humans, mice, fin whales, vaquitas, wolves, collared flycatchers, pied flycatchers, halictid bees, Drosophila, and mosquitoes. We found that mammals have a higher proportion of strongly deleterious mutations (defined as s≤-0.01; 22% to 47% in mammals; 0.0% to 5.4% in insects and birds) and a lower proportion of weakly deleterious mutations than insects and birds. Further, the DFE co-varies with phylogeny, such that the mean mutation effects are more similar in closely related species (Pagel's λ = 0.84, P = 0.01). Next, we investigated whether various summary statistics of the DFE were related to variation in life-history traits across these organisms. We found some support for genome size, body mass, and long-term effective population size being correlated with the DFE. Overall, our findings are consistent with predictions derived independently from the Fisher's Geometric Model (FGM), which defines organismal complexity as the number of phenotypes under selection. FGM predicts that mutations are more deleterious in complex organisms, while strongly deleterious mutations occur more frequently in smaller populations. Our study demonstrates strong phylogenetic signal in the evolution of a fundamental population genetics parameter, and proposes that, through mechanisms of epistasis, long-term population size and organismal complexity could be underlying variation in the DFE across animals.

Indexed as

Genetic FitnessMutationPhylogenyAnimalsBirdsEvolution, MolecularGenetics, PopulationHumansSelection, Genetic

Identifiers

PMID42709909
PMCPMC13592725

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.