Evidence map›Paper›PMID 42313750›Full record

ArticlePLoS biology2026

Levels of additive genetic variation vary substantially between species.

Lillith C Zijmers, Katie L Abson, Jarrod D Hadfield, Adam Eyre-Walker

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Nucleotide diversity is a poor predictor of short-term adaptive potential.Proceedings of the National Academy of Sciences of the United States of America · 2026
    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

4 authors.

Lillith C ZijmersSchool of Life Sciences, University of Sussex, Brighton, United Kingdom.ORCID https://orcid.org/0009-0004-8643-3778
Katie L AbsonInstitute of Ecology and Evolution, University of Edinburgh, Edinburgh, United Kingdom.
Jarrod D HadfieldInstitute of Ecology and Evolution, University of Edinburgh, Edinburgh, United Kingdom.
Adam Eyre-WalkerSchool of Life Sciences, University of Sussex, Brighton, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A population's ability to adapt is determined by its levels of additive genetic variance (VA), and while it is agreed that most organisms have genetic variation for most traits, the extent to which it varies between species is poorly characterized. Here, we investigate this question by compiling 3,209 and 1,852 estimates of heritability and evolvability (the additive genetic variance divided by the square of the mean), respectively, for a variety of traits from 220 and 172 multicellular eukaryotic species. Using phylogenetic generalized linear mixed models, we find substantial and highly significant interspecific variation in evolvability. Much of the variation is explained by phylogenetic relatedness, with plants in our data having substantially higher evolvability than animals. While heritability also varies between species, the differences are more subtle, and plants are not exceptional. We investigate whether the variation in evolvability and heritability between species is due to variation in the mutation rate, effective population size, genome size, ploidy, and recombination rate, but find little evidence of any factor being important. However, the confidence intervals are large suggesting that we have little power to detect any associations between these factors and our estimates of VA.

Indexed as

Genetic VariationAnimalsBiological EvolutionEvolution, MolecularModels, GeneticMutation RatePhylogenyPlantsPloidiesSpecies Specificity

Identifiers

PMID42313750
PMCPMC13278422

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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.