Genetic and genomic evaluation of different breeding strategies using stochastic simulation in Iranian buffalo (

Abstract

Context Despite the importance of buffalos to income and food needs, there has been little attention to the simulation of breeding programs using different strategies in the Iranian buffalo population. Aims The present study aimed to evaluate different breeding strategies in Iranian native buffalo by using stochastic simulation, and to determine the most appropriate strategy for Iranian buffalo breeding. Methods Different breeding scenarios were simulated for sensitivity of outcomes to the nucleus population size and selection design. Two systems of closed and open nucleus breeding schemes were simulated. Three different nucleus sizes, the optimal fraction of nucleus dams born in the base, and the appropriate fraction of base sires born in the nucleus were considered. Four selection designs were considered: random, phenotypic, best linear unbiased prediction (BLUP), and genomic selection. Key results The results indicated that in different population sizes and both open and closed nuclei, the average total genetic value was higher in genomic selection than in other selection designs. The total genetic value was higher in open nucleus than closed nucleus breeding schemes regardless of selection design. The highest mean of total genetic value was estimated at 91.53 in the optimal nucleus size of 15% of base population for the genomic selection approach and the open nucleus breeding system. In all nucleus population sizes, the highest inbreeding was obtained for selection based on BLUP, followed by genomic, phenotype and then random selection. Conclusions Overall, the application of open nucleus breeding schemes along with genomic selection is recommended for improving buffalo productivity. Implications Selection strategies used in Iranian buffaloes have so far been based on phenotypic information; however, obtaining genetic information could improve genetic progress in the Iranian buffalo population.