Optimum contribution for mate selection in Santa Inês sheep

Abstract

The objective of this research was to simulate the genetic gains expected comparing random mating strategies and mate selection by optimum contribution with different penalty levels in the inbreeding rate of Santa Inês sheep. The optimum contribution theory was thus applied to optimize genetic gain in the long term in twelve selection groups by selectively mating 500 females with the respective males, increasingly penalizing the increase in inbreeding in the objective function. Genetic algorithms were used to find the optimum contribution. Optimization was achieved via EVA software. Selection candidates had their contribution defined into four treatments, using different values to weigh the genetic merit and penalize increases in inbreeding. This made it possible to measure the degree of control over those parameters that can be obtained with this methodology. This selection offers different levels of genetic gain, which are achievable from restrictions on the coancestry. The number of males selected and their distribution into selection groups varied according to the penalty attributed to inbreeding in the objective function. Mate selection using optimum contribution should be adopted when aiming to limit the increase in inbreeding. Increasing the exchange of genetic material between groups is recommended to elevate genetic gain and maintain control over inbreeding.

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“…In the early generations, inbreeding was below the imposed 10% restriction, which allowed Mate Selection to indicate lower numbers of sires in the first generations. In studies that evaluated scenarios with increasing levels of inbreeding, the number of sires increased as restriction increased [12,25,26]. In a way, this corroborates with the result of this study, since the level of inbreeding increased over generations (Figure 2).…”

“…Mating systems may cause genetic progress in the early generations, however, over time, this progress tends to be only maintained [37]. In other studies, directed mating softwares (algorithms) presented greater genetic progress when compared to real situations [26,38] and to random mating [39].…”