Genomic prediction in Japanese Black cattle: Application of a single-step approach to beef cattle

Abstract

The implementation of genomic selection for Japanese Black cattle, known for rich marbling of their meat, is now being explored. Although multiple-step methods are often adopted for dairy cattle, they present shortcomings such as bias and loss of information in addition to operational complexity. These can be avoided using single-step genomic BLUP (ssGBLUP) based on the relationship matrix H, which is constructed from the numerator relationship matrix (A) augmented by the genomic relationship matrix (G). This study assessed the use of ssGBLUP for 3 economically important traits in Japanese Black cattle. Three aspects of ssGBLUP that are important for practical use were examined specifically: the mixing proportions of blending G with A, selection of subsets of genotyped animals used for constructing H, and prediction ability for ungenotyped animals. Different mixing proportions were tested to assess the influence of these proportions on variance component estimation and prediction accuracy. For all traits, the highest or nearly highest accuracy was obtained when the adopted mixing proportion provided heritability closest to that inferred based on A. However, the accuracy did not increase greatly under adjustment of the mixing proportion, thereby suggesting that the influence of the mixing proportion on the accuracy was limited. Genotype data of influential bulls showed a greater contribution to accuracy than that of bulls that were less influential. Genotyping animals with phenotypic records increased the accuracy. It can be prioritized over genotyping bulls that are not influential on the population. These results are expected to present good guides to the future expansion of genotyped populations. Even for animals without genotype data but with genotyped sires, ssGBLUP provided more accurate prediction than BLUP did. For both phenotype and breeding value prediction, ssGBLUP provides more accurate prediction than BLUP, suggesting its usefulness in genomic selection in Japanese Black cattle.

Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture

“…In general, the realized accuracies of DGV for the four carcass traits, regardless of the method used, were low compared with results from other studies [16, 25, 50]. One of the main reasons for the lower accuracies observed in our study could be due to the small training population size ( ≈ 946) and the large effective population size ( = 224) for the Hanwoo breed.…”

“…However, estimated heritabilities for BT and MS were higher (+9 and +11.3, respectively) than those in the study of Lee et al [7]. In Japanese Black cattle, Onogi et al [50] reported similar heritabilities for EMA (0.43) and MS (0.66) but a higher heritability for CW (0.56) than our study. In a study on the Angus breed, Saatchi et al [25] reported higher heritabilities for CW and EMA and lower heritabilities for BT and MS than those found here.…”

“…1a, 2a), which is consistent with the results of Onogi et al [50]. To investigate further the low prediction accuracy for BT and MS, genomic heritability () was estimated for each trait and with each method (Table 4).…”

“…This is the most challenging scenario for genomic prediction because a large part of the accuracy of DGV results from genetic relationships captured by SNPs [72]. This could explain that our prediction accuracies were lower than those reported by Onogi et al [50] for which the number of genotyped animals was larger and the effective population size was smaller [64] than in our study.…”

“…Using a training population of about 2000 animals in multi-breed commercial beef cattle, Rolf et al [16] observed that the highest accuracies of DGV for BT, CW, EMA and MS were equal to 0.51, 0.78, 0.60 and 0.76, respectively. Onogi et al [50] reported a predicted ability (correlation between the DGV and the adjusted phenotypes) of 0.44, 0.42 and 0.39 for CW, EMA and MS, respectively. In our study, the genetic relationship between the validation and reference populations was close to zero.…”