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Allele frequencies and selection coefficients in locally adapted populations

Sibly, R. M. ORCID: and Curnow, R. N. (2023) Allele frequencies and selection coefficients in locally adapted populations. Journal of theoretical biology, 565. 111463. ISSN 1095-8541

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To link to this item DOI: 10.1016/j.jtbi.2023.111463


Understanding the role of natural selection in driving evolutionary change requires accurate estimates of the strength of selection acting at the genetic level in the wild. This is challenging to achieve but may be easier in the case of populations in migration-selection balance. When two populations are at equilibrium under migration-selection balance, there exist loci whose alleles are selected different ways in the two populations. Such loci can be identified from genome sequencing by their high values of F . This raises the question of what is the strength of selection on locally-adaptive alleles. To answer this question we analyse a 1-locus 2-allele model of a population distributed between two niches. We show by simulation of selected cases that the outputs from finite-population models are essentially the same as those from deterministic infinite-population models. We then derive theory for the infinite-population model showing the dependence of selection coefficients on equilibrium allele frequencies, migration rates, dominance and relative population sizes in the two niches. An Excel spreadsheet is provided for the calculation of selection coefficients and their approximate standard errors from observed values of population parameters. We illustrate our results with a worked example, with graphs showing the dependence of selection coefficients on equilibrium allele frequencies, and graphs showing how F depends on the selection coefficients acting on the alleles at a locus. Given the extent of recent progress in ecological genomics, we hope our methods may help those studying migration-selection balance to quantify the advantages conferred by adaptive genes.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:111391
Uncontrolled Keywords:Population genetics, F(ST), Ecological genomics, Migration-selection balance, Dispersal

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