The divergence of mean phenotypes under persistent Gaussian selection.
Although multigenic traits are often assumed to be under some form of stabilizing selection, numerous aspects of the population-genetic environment can cause mean phenotypes to deviate from presumed optima, often in ways that effectively transform the fitness landscape to one of directional selectio...
| Publicado en: | Genetics Vol. 229; no. 4; pp. 1 - 18 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184598637&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184598637 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: Apr2025 vid: 229 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184598637 184598637 184598637 10.1093/genetics/iyaf031 184598637 ppf: 1 ppct: 17 formats: tig: atl: The divergence of mean phenotypes under persistent Gaussian selection. aug: au: Lynch, Michael Menor, Scott affil: Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85287, USA sug: subj: Phenotype Evaluation Algorithms Gene Expression Profiling Mutation Genomics Data Analysis, Statistical Human Hypothesis Genotype Genetic Profile Computer Simulation DNA RNA Alleles Descriptive Statistics Funding Source ab: Although multigenic traits are often assumed to be under some form of stabilizing selection, numerous aspects of the population-genetic environment can cause mean phenotypes to deviate from presumed optima, often in ways that effectively transform the fitness landscape to one of directional selection. Focusing on an asexual population, we consider the ways in which such deviations scale with the relative power of selection and genetic drift, the number of linked genomic sites, the magnitude of mutation bias, and the location of optima with respect to possible genotypic space. Even in the absence of mutation bias, mutation will influence evolved mean phenotypes unless the optimum happens to coincide exactly with the mean expected under neutrality. In the case of directional mutation bias and large numbers of selected sites, effective population sizes ( N e ) can be dramatically reduced by selective interference effects, leading to further mismatches between phenotypic means and optima. Situations in which the optimum is outside or near the limits of possible genotypic space (e.g. a half-Gaussian fitness function) can lead to particularly pronounced gradients of phenotypic means with respect to N e , but such gradients can also occur when optima are well within the bounds of attainable phenotypes. These results help clarify the degree to which mean phenotypes can vary among populations experiencing identical mutation and selection pressures but differing in N e , and yield insight into how the expected scaling relationships depend on the underlying features of the genetic system. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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