Optimisation of variance component estimation and genomic prediction in a commercial crossbred population of Duroc x (Landrace x Yorkshire) three-way pigs

Abstract

Crossbreeding is often used in livestock breeding, and genomic selection (GS) is implemented with the breeding goal of selecting purebreds (PB) with high genetic merit for hybridisation to produce crossbreds (CB) with generally improved performance. Previous studies have demonstrated the practicality and efficiency of using CB progeny from a commercial population as a reference population for GS, where a reference population consisting of extreme phenotypic individuals showed a predictive advantage. However, this completely extreme sampling strategy would significantly overestimate the genetic variance of traits, resulting in a significant inflation of the genomic estimated breeding values (GEBV) of PB candidates. So, we explored and optimised the variance component (VC) estimation and genomic prediction using different sampling strategies in a commercial CB population based on data from a Duroc x (Landrace x Yorkshire) pigs three-way crossbreeding system. We first compared the performance of completely extreme sampling, completely random sampling, and four mixed sampling schemes combining extreme and random sampling for VC estimation and genomic prediction for traits with high, medium, and low heritability ($h^2$ = 0.5, 0.3, and 0.1) at different sample sizes (500–6 500). The results showed that the VC estimated from the reference populations obtained using mixed sampling strategies was more accurate than completely extreme sampling, and the mixed reference populations can carry out more accurate predictions and achieve higher response to selection. Furthermore, we applied an optimisation strategy for the mixed reference populations by solving the mixed model equation based on the VC estimated from only random CB therein, which proved to be very positive for improving the GEBV inflation caused by extreme phenotypic CB, effectively reducing the prediction bias while ensuring the prediction accuracy and response to selection. The combination of accurate VC estimation from random CB and the advantage of extreme phenotypic CB in prediction accuracy allows the mixed reference populations to achieve a superior predictive performance in GS. The optimised strategies can maximise the information from commercial CB populations in livestock genomic breeding.