Comparison of conventional BLUP and single-step genomic BLUP evaluations for yearling weight and carcass traits in Hanwoo beef cattle using single trait and multi-trait models

Abstract

Hanwoo, an important indigenous and popular breed of beef cattle in Korea, shows rapid growth and has high meat quality. Its yearling weight (YW) and carcass traits (backfat thickness, carcass weight- CW, eye muscle area, and marbling score) are economically important for selection of young and proven bulls. However, measuring carcass traits is difficult and expensive, and can only be performed postmortem. Genomic selection has become an appealing procedure for genetic evaluation of these traits (by inclusion of the genomic data) along with the possibility of multi-trait analysis. The aim of this study was to compare conventional best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) methods, using both single-trait (ST-BLUP, ST-ssGBLUP) and multi-trait (MT-BLUP, MT-ssGBLUP) models to investigate the improvement of breeding-value accuracy for carcass traits and YW. The data comprised of 15,279 phenotypic records for YW and 5,824 records for carcass traits, and 1,541 genotyped animals for 34,479 single-nucleotide polymorphisms. Accuracy for each trait and model was estimated only for genotyped animals by five-fold cross-validation. ssGBLUP models (ST-ssGBLUP and MT-ssGBLUP) showed ~19% and ~36% greater accuracy than conventional BLUP models (ST-BLUP and MT-BLUP) for YW and carcass traits, respectively. Within ssGBLUP models, the accuracy of the genomically estimated breeding value for CW increased (19%) when ST-ssGBLUP was replaced with the MT-ssGBLUP model, as the inclusion of YW in the analysis led to a strong genetic correlation with CW (0.76). For backfat thickness, eye muscle area, and marbling score, ST- and MT-ssGBLUP models yielded similar accuracy. Thus, combining pedigree and genomic data via the ssGBLUP model may be a promising way to ensure acceptable accuracy of predictions, especially among young animals, for ongoing Hanwoo cattle breeding programs. MT-ssGBLUP is highly recommended when phenotypic records are limited for one of the two highly correlated genetic traits.

Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

“…It was clear that the inclusion of genomic information in the analysis can significantly improve prediction accuracy relative to traditional pedigree-based models due to capturing variation in Mendelian sampling [16]. The superiority of ssGBLUP over pedigree-based BLUP or GBLUP approaches for the prediction of breeding values has been reported in previous studies on Hanwoo cattle [18,26,38] and other beef cattle breeds [30,33,39]. Other studies on French dairy goats [23,40] and sheep [24] identified that ssGBLUP was more accurate than either the pedigree-based BLUP or GBLUP for milk production traits, udder type traits, and somatic cell scores.…”

“…In Hanwoo beef cattle, carcass traits (backfat thickness (BFT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS)), and yearling weight (YW) are economically important traits used to select young and proven bulls [ 25 ]. Since there is enriched traditional pedigree information available in Hanwoo [ 26 ], it is expected that the use of distinct WssGBLUP alternatives [ 6 ] can be utilized to improve genomic prediction accuracy for traits with different genetic architectures [ 7 ]. Nonetheless, these methods have not yet been comprehensively investigated in breeding schemes for this breed.…”

Integrating Single-Step GWAS and Bipartite Networks Reconstruction Provides Novel Insights into Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

“…A total of 136 genes were annotated in these genomic regions for CW and, according to NCBI, some of them were associated with EMA and YW. This was expected due to the high positive genetic correlation between CW and YW (0.76) and between CW and EMA (0.56), which was estimated in Hanwoo cattle [ 31 ], indicating that the same genes were controlling these traits. The most relevant pleiotropic QTL was identified on chromosome 14 at 24.58–25.33 Mb, which explained 17.66% of additive genetic variance in CW, 7.98% in EMA, and 9.96% in YW.…”

“…Out of 45,304 SNPs, the quality control of genomic data excluded the SNPs with an unknown position (302 SNPs) and those located on sex chromosomes (1150 SNPs). Additionally, the SNPs with call rates lower than 0.98 (2677 SNPs), minor allele frequencies lower than 0.01 (6684 SNPs), and a maximum difference between the observed and expected frequency of 0.15 as the departure of heterozygous from Hardy–Weinberg equilibrium (31 SNPs) were excluded from the analyses [ 30 , 31 ]. In the final analysis, genotypes on 34,460 SNP markers from 1540 animals (385 bulls and 1155 steers) were available.…”

“…The WssGBLUP was performed using BLUPF90 family software [ 32 ]. First, variance components were determined using the pedigree-based univariate model obtained by Mehrban et al [ 31 ]. A single-trait animal model to predict genomic breeding values was as follows:…”