A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population

• Published in: Genetics selection evolution (Paris) Vol. 55; no. 1; p. 34
• Main Authors: Guillenea, Ana, Lund, Mogens Sandø, Evans, Ross, Boerner, Vinzent, Karaman, Emre
• Format: Journal Article
• Language: English
• Published: France BioMed Central Ltd 15.05.2023; BioMed Central; BMC
• Subjects: Accuracy; Alleles; Allelomorphism; Animal breeding; Animals; Beef cattle; Bias; Business performance management; Cattle; Cattle - genetics; Chromosomes; Correlation; Genomes; Genomics; Genomics - methods; Genotype; Life Sciences; Methods; Models, Genetic; Phenotype; Phenotypes; Polymorphism, Single Nucleotide; Single-nucleotide polymorphism; Software
• ISSN: 1297-9686; 0999-193X; 1297-9686
• DOI: 10.1186/s12711-023-00806-1

Recently, crossbred animals have begun to be used as parents in the next generations of dairy and beef cattle systems, which has increased the interest in predicting the genetic merit of those animals. The primary objective of this study was to investigate three available methods for genomic prediction of crossbred animals. In the first two methods, SNP effects from within-breed evaluations are used by weighting them by the average breed proportions across the genome (BPM method) or by their breed-of-origin (BOM method). The third method differs from the BOM in that it estimates breed-specific SNP effects using purebred and crossbred data, considering the breed-of-origin of alleles (BOA method). For within-breed evaluations, and thus for BPM and BOM, 5948 Charolais, 6771 Limousin and 7552 Others (a combined population of other breeds) were used to estimate SNP effects separately within each breed. For the BOA, the purebreds' data were enhanced with data from ~ 4K, ~ 8K or ~ 18K crossbred animals. For each animal, its predictor of genetic merit (PGM) was estimated by considering the breed-specific SNP effects. Predictive ability and absence of bias were estimated for crossbreds and the Limousin and Charolais animals. Predictive ability was measured as the correlation between PGM and the adjusted phenotype, while the regression of the adjusted phenotype on PGM was estimated as a measure of bias. With BPM and BOM, the predictive abilities for crossbreds were 0.468 and 0.472, respectively, and with the BOA method, they ranged from 0.490 to 0.510. The performance of the BOA method improved as the number of crossbred animals in the reference increased and with the use of the correlated approach, in which the correlation of SNP effects across the genome of the different breeds was considered. The slopes of regression for PGM on adjusted phenotypes for crossbreds showed overdispersion of the genetic merits for all methods but this bias tended to be reduced by the use of the BOA method and by increasing the number of crossbred animals. For the estimation of the genetic merit of crossbred animals, the results from this study suggest that the BOA method that accommodates crossbred data can yield more accurate predictions than the methods that use SNP effects from separate within-breed evaluations.