Optimizing the gain of social genetic effect under the control of inbreeding using genetic algorithm

• A socially affected trait with different social effect size were simulated.
• Two methods based on genetic algorithm were used to restrict the inbreeding.
• Two optimization methods resulted in lower inbreeding rate than common method.
• Two optimization methods resulted in more genetic gain than common method.

Social interactions among animals are widely existed in livestock population. However, some studies showed that the selection of social genetic effect leaded to extra increase of inbreeding. In this study, two optimization methods (SBLUP+GA1, SBLUP+GA2) based on genetic algorithm were used to obtain the optimal genetic contributions of seedstocks and maximize the average genetic gains of direct and social genetic component while minimizing the inbreeding. In SBLUP+GA1, only the contributions of sires were optimized. In SBLUP+GA2, the contributions of sires and dams were optimized together. The results showed SBLUP+GA1 and SBLUP+GA2 resulted in 18.52% and 25.93% lower inbreeding rate than common social genetic effect selection based on BLUP method (SBLUP) under base parameters, respectively. Under that situation, the average gains for direct, social and total genetic effect component in SBLUP+GA1 were actually improved 3.59%, 10.02% and 4.32% relative to SBLUP, respectively. In SBLUP+GA2, they were 1.28%, 10.00% and 2.02%, respectively. SBLUP+GA2 resulted in lower inbreeding rate, but, obtained slightly less genetic gain than SBLUP+GA1.