Predicting cumulated response to directional selection in finite panmictic populations

• Published in: Theoretical and applied genetics Vol. 79; no. 6; pp. 833 - 840
• Main Authors: Verrier, E, Colleau, J.J, Foulley, J.L
• Format: Journal Article
• Language: English
• Published: Germany 01.06.1990
• Subjects: additive infinitesimal model; additive model; algorithms; animal breeding; directional selection; genetic improvement; genetic variance; heritability; inbreeding; Monte Carlo method; panmictic population; selection response
• ISSN: 0040-5752; 1432-2242
• DOI: 10.1007/BF00224253

Accurate prediction of the cumulated genetic gain requires predicting genetic variance over time under the joint effects of selection and limited population size. An algorithm is proposed to quantify at each generation the effects of these factors on average coefficient of inbreeding, genetic variance, and genetic mean, under a purely additive polygenic model, with no mutation, and under the assumption of absence of inbreeding depression on viability affecting selection differentials. This algorithm is relevant to populations where mating is at random and generations do not overlap. It was tested via Monte Carlo simulation on a population of 3 males and 25 females mass selected out of 50 candidates of each sex, over 30 generations. For two values of the initial heritability of the selected trait, 0.5 and 0.9 (to represent high accuracy in index selection), predicted values of the genetic variance are in agreement with observed results up to the 12th and 19th generations, respectively. Beyond these generations, the variance is overestimated, due to an underestimation of the effect of selection on the rate of inbreeding. Finally, the algorithm provides predictions of the cumulated responses close to the observed values in both selected populations. It is concluded that, as regards the hypotheses of the study, the proposed algorithm is satisfactory, and could be used to optimize selection methods with respect to the cumulated genetic gain in the mid- or long-term. Possible extensions of the algorithm to more realistic situations are discussed.