Evaluation of genomic mating approach based on genetic algorithms for long-term selection in Huaxi cattle

Abstract

Background: Genomic mating (GM) can effectively control the growth rate of inbreeding in population and achieve long-term sustainable genetic progress. However, the design of GM method and assessment of its effects during long-term selection have not been fully explored in beef cattle breeding.

Results: In this study, we constructed a simulated population based on the real genotypes of Huaxi cattle, where five generations of simulated breeding were carried out using the genomic optimal contribution selection (GOCS), genetic algorithms strategy and three traditional mating strategies. During the breeding process, genetic parameters including average genomic estimated breeding value (GEBV), genetic gain values ( $\Delta G$ ), the rate of inbreeding values ( $\Delta F$ ) were calculated and compared across generations. Our results showed that the GM method could significantly improve the genetic gain while effectively controlling the inbreeding accumulation within the population. When using the GM method, there was an increase in genetic gain for Huaxi cattle ranging from 1.1% to 25.6% compared to traditional mating strategy, with inbreeding decreasing in the range of 5.8% to 36.2%. Validation using the real dataset from Huaxi cattle further confirmed our findings from the simulated study, offspring populations using the GM strategy exhibited a 7.3% increase in genetic gain compared to positive assortative mating.

Conclusions: These findings suggest that the GM method shows potential for achieving sustainable genetic gain and could be utilized during long-term selection in beef cattle breeding.

Keywords: Genetic algorithms; Genetic gain; Genomic mating; Huaxi cattle; Simulation.