The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

Abstract

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L SB ) and that for the production of cows (L SC ) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L DB ) and cows (L DC ) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.a These authors contributed equally to this work.

Genomic evaluation of Brown Swiss dairy cattle with limited national genotype data and integrated external information

“…Regardless of sex, the GEBV f of a young individual without phenotypic information in this study, estimated by using small national training population in the single-step genomic evaluation, was, on average, almost 10 points higher than predictions based on EBV (Figure 4). In contrast to the report on milk yield by Lee et al (2020), who estimated 0.47 to 0.52 GEBV REL for females with phenotype, and 0.68 to 0.75 GEBV REL for sires with progeny, we estimated higher mean GEBV f REL for females with phenotypic information (0.68-0.83). Our estimated mean GEBV f REL for males with daughter phenotype was somewhat lower, presumably due to the smaller amount of daughter information (0.58-0.65; Figure 4).…”

“…The GEBVM f REL increase was more prominent for males. Lee et al (2020) obtained an average of 9 points of GEBV REL increase compared with EBV for milk production traits estimated using the ssGBLUP method with ~2,000 genotyped Holstein individuals of both sexes, which corresponded to the difference between EBV f and GEBV f REL for males in this study (Table 5). They noted lower REL increase for progeny-tested bulls (4 points) and females with phenotype information (7 points), and higher REL increase for heifers without own and bulls without progeny phenotypic information (13 and 17 points, respectively).…”

“…Our estimated mean GEBV f REL for males with daughter phenotype was somewhat lower, presumably due to the smaller amount of daughter information (0.58-0.65; Figure 4). However, for both young females and young males without phenotypic information, our mean GEBV f REL, between the range of 0.43 and 0.5 (0.42-0.51) for females (males; Figure 4), were higher than those for young females without phenotypic information (0.34-0.38) and males without progeny (0.35-0.39) reported by Lee et al (2020). Their heritability for milk yield, ranging between 0.16 and 0.28 for different parities, was lower than ours (0.33); that may explain lower REL values for the younger animals in their study.…”

Heritability and Repeatability Estimates for Milk Production Traits Using Phenotypic, Pedigree-Based and Genomic Data of Serbian Holstein Cows

“…This may be due to the fact that MY is the trait with the highest selection pressure over a longer period of time compared to other traits where higher heritability values were calculated. In the research by Lee et al (2020), the estimated heritability of milk yield per parity in the first, second and third parity was 0.28, 0.20 and 0.16, respectively, while for fat yield it was 0.26, 0.23 and 0.20, and for protein yield it was 0.23, 0.18, and 0.15, respectively. The highest heritability estimates for milk fat percentage and protein percentage, as was also the case in our study, were calculated by Oliveira Junior et al ( 2021), but their values were much higher (0.66 and 0.69, respectively) and the applied model was a bivariate linear animal model using Bayesian methods via Gibbs sampling.…”

Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep

“…With the publication of the whole-genome sequence of dairy cows and the continuous upgrading of commercial SNP microarrays, GP and GS have been adopted on a large scale in conventional breeding programs of dairy cows ( 17 , 18 ). This has also promoted the development of SNP microarrays for a variety of other livestock.…”