Mean fitness is maximized in small populations under stabilizing selection on highly polygenic traits

Clicks: 22
ID: 329173
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #296 of 400 articles by views in current genetics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 400 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Stabilizing selection commonly acts on complex traits that affect individual fitness. Here, we relate mean fitness under stabilizing selection to population size and trait architecture, using a simple application of theoretical predictions for the distribution of phenotypic values in a single-trait Gaussian stabilizing selection model. We show that mean fitness is maximized by a finite (often small) population size when the total diploid mutation rate U across trait-affecting loci is reasonably large. Namely, this occurs when U>σ24(VS+VE), where σ2 is the variance of the distribution of effect sizes of new mutations, VS determines the strength of stabilizing selection, and VE is the environmental variance. We validate these predictions using simulations and briefly discuss their implications for interpreting genetic load and adaptability in small populations.
Reference Key
openalex_W4416385829 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal current genetics
Year 2026
DOI
10.1093/genetics/iyag254
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.