Inferring the trajectory of genetic variance in the course of artificial selection.
A method is proposed to infer genetic parameters within a cohort, using data from all individuals in an experiment. An application is the study of changes in additive genetic variance over generations, employing data from all generations. Inferences about the genetic variance in a given generation are based on its marginal posterior distribution, estimated via Markov chain Monte Carlo methods. As defined, the additive genetic variance within the group is directly related to the amount of selection response to be expected if parents are chosen within the group. Results from a simulated selection experiment are used to illustrate properties of the method. Four sets of data are analysed: directional selection with and without environmental trend, and random selection, with and without environmental trend. In all cases, posterior credibility intervals of size 95% assign relatively high density to values of the additive genetic variance and heritability in the neighbourhood of the true values. Properties and generalizations of the method are discussed.
Sorensen
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Department of Animal Science, Iowa State University, Ames, IA 50011-3150, USA. snfds@genetics.agrsci.dk
Fernando
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Gianola
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D000818:Animals; D004777:Environment; D014644:Genetic Variation; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D012641:Selection, Genetic