Monte Carlo simulation models of breeding-population advancement.

Abstract

Five generations of population improvement were modeled using Monte Carlo simulations. The model was designed to address questions that are important to the development of an advanced generation breeding population. Specifically we addressed the effects on both gain and effective population size of different mating schemes when creating a recombinant population for recurrent selection. Five different mating schemes were modeled with recurrent selection, 4 control-pollinated (CP) and one open-pollinated (OP) scheme. The CP schemes were: 2 random mating (RM) designs, 1 with 2 crosses per parent and 1 with 8 crosses per parent; and imbalanced parental contribution schemes including assortative mating (AM), and random mating with an 'elite' nucleus (EN). Genetic gain is maximized in the mating scheme that uses random mating and increases the selection differential for recombinant cross selection. The imbalanced designs (AM and EN) increased gain only slightly over random mating. The model was also used to look at the erosion of the breeding population base when applying different restrictions on selection to the CP treatments. RM offered the highest levels of gain for a given effective population size. AM was little different to RM but there was quite rapid attrition of the genetic base for on-going breeding with the EN scheme. The trade-off between maximizing short-term gain with family selection and maintaining genetic diversity for long-term potential within the framework of a fixed resource is discussed.

Citation

King, J.N.; Johnson, G.R. 1993. Monte Carlo simulation models of breeding-population advancement. Silvae Genetica. 42(2-3): 68-78