Simulation controlling inbreeding for selection experiments on woody breast meat in broiler

Abstract The purpose of this study was to control inbreeding for selection experiments on woody breast (WB) meat in broiler. The simulation was designed to figure out which mating plan would show proper breeding values while optimizing inbreeding assuming that a selection study would be done on WB in broilers starting with 500 males and 500 females as a foundational population. The simulated selections were based on Optimum Genetic Contribution theory (OGC) under different conditions over 10 generations, which uses relationships among individuals as weighting factors. It is selecting individuals by weighting estimated breeding values with average relationships among individuals. From the 2nd generation, various selection plans were considered in each sex, which were top 10, 20, 50 and 100 males selected, and top 100 and 200 females selected every generation. Each female bird was assumed to have 10 eggs. The algorithm is as follows: 1) Identify the individual having the best EBV; 2) Calculate average relationships between selected and candidates; 3) Select the individual having the best EBV adjusted for average relationships using the weighting factor k; 4) Repeat process until the number of individuals selected equals number required. Three different weighting values (k=0, 1, 2) were used, which made a total 24 different conditions compared (4×2×3). Additive genetic variance of breast meat was 1.134. Mendelian sampling terms were also considered when the breeding values were generated. Results showed that higher k value (k=2) controlled effectively inbreeding and maintained consistent increases in selection response. Differences in breeding values among selection plans with OGC algorithm and by EBV only was 4% on average; however, average rate of inbreeding (0.1) was controlled by 27% after 10 generations. These results indicate that the OGC algorithm can be used effectively in a short-term selection program with the relatively smaller number of populations.