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Simulated Selection in Autotetraploid Populations II. Effects of Double Reduction, Population Size, and Selection Intensity 1
Author(s) -
Swanson M. R.,
Dudley J. W.,
Carmer S. G.
Publication year - 1974
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci1974.0011183x001400050007x
Subject(s) - biology , heritability , selection (genetic algorithm) , epistasis , population , truncation selection , genetic variation , stabilizing selection , genetic gain , directional selection , natural selection , quantitative genetics , genetic model , effective population size , additive genetic effects , genetic drift , genetic correlation , statistics , genetics , mathematics , gene , demography , artificial intelligence , sociology , computer science
Effects of double reduction, population size, and selection intensity on response to mass selection in a random mating autotetraploid population were studied by computer simulation. Results were compared to diploids. Selection was based on a single quantitative trait controlled genetically by 40 loci, ignoring the effects of mutation, migration, and natural selection. Six genetic models, with varying levels of additive, dominance, and epistatic variance were compared. In models with small dominance effects, double reduction increased heritability by increasing additive genetic variance, thus increasing selection response. In models with dominance deviations, selection response was not affected by double reduction. Genetic drift was a major factor in limiting total selection response when the effective population size was 10 or less. Diploids were affected more by genetic drift than autotetraploids. Changes in genetic variance over generations of selection varied with the genetic model, selection intensity, and initial gene frequency. Peculiar trends were noted for some models due to a mix of initial gene frequencies. Of several measures of heritability compared, parent‐offspring regression on a random sample of parents and their offspring predicted selection gain best.

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