Parameterizing the genetic architecture under stabilizing selection

Clicks: 4
ID: 320946
2026
Free PDF
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 #167 of 320 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 320 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
Sign in to mint
Abstract
Abstract Across many complex traits, genetic variants with larger effect sizes tend to occur at lower frequencies, which is often interpreted as a signature of stabilizing selection. In statistical genetics, the so-called α-model captures this relationship by assuming that effect size variance is inversely proportional to heterozygosity raised to a power 0 ≤ α ≤ 1. Although empirically useful, the α-model is phenomenological rather than mechanistic and lacks a direct population genetic interpretation. In this paper, we derive an alternative to the α-model based on evolutionary theory. Our approach yields a linear mixed model in which frequency dependence emerges naturally as a function of interpretable evolutionary quantities describing mutational variance, selection intensity, and coupling between the focal and selected traits. These quantities enter through two identifiable variance components that can be estimated by restricted maximum likelihood (REML). The resulting framework links a fitness landscape model to standard mixed-model methodology, enabling both inference on evolutionary parameters and downstream prediction by best linear unbiased prediction (BLUP). In forward simulations, the model accurately recovers the focal-trait variance and generally improves genetic prediction relative to conventional α-model baselines.
Reference Key
openalex_W7168298488 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hanbin Lee, Jonathan Terhorst
Journal current genetics
Year 2026
DOI
10.1093/genetics/iyag180
URL
https://doi.org/10.1093/genetics/iyag180
Keywords Keywords not found

Citations

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

Comments

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