Abstract
A program to determine optimum contribution selection using differential evolution was developed. The objective function to be optimized was composed by the expected merit of the future progeny and the coancestry among selected parents. Simulated and real datasets of populations with overlapping generations were used to validate and test the performance of the program. The program was computationally efficient and feasible for practical applications. The expected consequences of using the program, in contrast to empirical procedures to control inbreeding and/or to selection based exclusively on expected genetic merit, would be the improvement of the selection response and a more effective control of inbreeding.
Highlights
Selection based on optimum genetic contribution (Woolliams & Thompson, 1994; Meuwissen, 1997) aims to restrict the inbreeding level of a population and to maximize the genetic progress in the long term
Under the same level of inbreeding, optimum contribution selection can provide higher genetic gain when compared to selection based exclusively on expected breeding value (Meuwissen & Sonesson, 1998)
The optimum genetic contribution was defined with the optimization of the following objective function (OF): OF = w1*x’EBV/2N + w2*x’Ax/4N2, where x = the vector of genetic contributions to be optimized, EBV = the vector containing the expected breeding values, A = the numerator relationship matrix, w1 and w2 = the weights for the expected merit of the future progeny (x’EBV/2N) and the average coancestry among selected parents (x’Ax/4N2), respectively, and N = the number of required matings
Summary
Selection based on optimum genetic contribution (Woolliams & Thompson, 1994; Meuwissen, 1997) aims to restrict the inbreeding level of a population and to maximize the genetic progress in the long term. Under the same level of inbreeding, optimum contribution selection can provide higher genetic gain when compared to selection based exclusively on expected breeding value (Meuwissen & Sonesson, 1998). The expected merit of the future progeny and the coancestry among selected parents are the usual components considered in the index (objective function) to be optimized in the optimum contribution selection definition. The objective function for optimum contribution selection can be optimized using, for example, Lagrange multipliers (Meuwissen & Sonesson, 1998), semidefinite programming (Pong-Wong & Woolliams, 2007) or evolutionary algorithms (Sorensen et al, 2006).
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