In populations with limited genotyping, single-step genomic best linear unbiased predictions (ssGBLUP) can produce biased or less accurate genomic predictions due to incompatibilities between genomic and pedigree relationship matrices. The study evaluated the impact of five alternative ssGBLUP models for genomic predictions of milk, fat, and protein yield production traits in South African Holstein cattle. The dataset included 696, 413 milk production records and pedigrees of 541, 325 animals. Production traits were 305-day lactation yields for milk, protein, and fat. Genotype data were based on the Illumina 50K chip v3, with 53, 218 SNPs. A total of 1221 animals with genotypes and 41, 407 SNP markers were in the final dataset. The five models used to estimate genomic estimated breeding values (GEBVs) were the single-step method (ssGBLUP), ssGBLUP accounting for inbreeding (ssGBLUPFx), ssGBLUP with unknown parent groups (ssGBLUPᵤpg), and two ssGBLUP models with blending, tuning, and scaling parameters set to optimum values in constructing the inverse of the unified relationship matrix (ssGBLUPₐdjusted). Realized prediction accuracies were highest for ssGBLUPₐdjusted models (6–7% improvements compared to ssGBLUP). Accuracy of GEBVs for milk, protein, and fat yields ranged from 0. 23, 0. 29, and 0. 30 for both ssGBLUP and ssGBLUPFx, 0. 26, 0. 32, and 0. 34 for ssGBLUPᵤpg, and 0. 29, 0. 35, and 0. 37 for ssGBLUPₐdjusted models, respectively. Corresponding bias, expressed as regression coefficients, ranged from 0. 30, 0. 31, and 0. 36 for ssGBLUP; 0. 31, 0. 32, and 0. 37 for ssGBLUPFx; 0. 41, 0. 44, and 0. 49 for ssGBLUPᵤpg; and 0. 44, 0. 47, and 0. 53 for ssGBLUPₐdjusted models, respectively. The improved accuracy and reduced bias observed with the ssGBLUPₐdjusted underscores the importance of optimizing the blending of pedigree- and genome-based relationships to achieve more reliable GEBVs, thereby improving selection decisions in Holstein dairy cattle.
Mafolo et al. (Fri,) studied this question.