Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Abstract

Genomic selection (GS) has been widely used in livestock, which greatly accelerated the genetic progress of complex traits. The population size was one of the significant factors affecting the prediction accuracy, while it was limited by the purebred population. Compared to directly combining two uncorrelated purebred populations to extend the reference population size, it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction. In this study, we simulated purebred offspring (PAS and PBS) and crossbred offspring (CAB) base on real genotype data of two base purebred populations (PA and PB), to evaluate the performance of genomic selection on purebred while incorporating crossbred information. The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship (REL) was better than the other methods (individuals closet or farthest to the purebred population, CP/FP) in term of the prediction accuracy. Furthermore, the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA, which was similar to combine PA and PAS. Moreover, the rank correlation between the multiple of the increased relationship (MIR) and reliability improvement was 0.60–0.70. But for individuals with low correlation (Cor(Pi, PA or B), the reliability improvement was significantly lower than other individuals. Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only. The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.