Jung-Woo Choi, Bong-Hwan Choi, Seung-Hwan Lee, Seung-Soo Lee, Hyeong-Cheol Kim, Dayeong Yu, Won-Hyong Chung, Kyung-Tai Lee, Han-Ha Chai, Yong-Min Cho, and Dajeong Lim. Mol. Cells 2015;38:466-73. https://doi.org/10.14348/molcells.2015.0019

Background: Hanwoo (Korean cattle), which originated from natural crossbreeding between taurine and zebu cattle, migrated to the Korean peninsula through North China. Hanwoo were raised as draft animals until the 1970s without the introduction of foreign germplasm. Since 1979, Hanwoo has been bred as beef cattle. Genetic variation was analyzed by whole-genome deep resequencing of a Hanwoo bull. The Hanwoo genome was compared to that of two other breeds, Black Angus and Holstein, and genes within regions of homozygosity were investigated to elucidate the genetic and genomic characteristics of Hanwoo.Results: The Hanwoo bull genome was sequenced to 45.6-fold coverage using the ABI SOLiD system. In total, 4.7 million single-nucleotide polymorphisms and 0.4 million small indels were identified by comparison with the Btau4.0 reference assembly. Of the total number of SNPs and indels, 58% and 87%, respectively, were novel. The overall genotype concordance between the SNPs and BovineSNP50 BeadChip data was 96.4%. Of 1.6 million genetic differences in Hanwoo, approximately 25,000 non-synonymous SNPs, splice-site variants, and coding indels (NS/SS/Is) were detected in 8,360 genes. Among 1,045 genes containing reliable specific NS/SS/Is in Hanwoo, 109 genes contained more than one novel damaging NS/SS/I. Of the genes containing NS/SS/Is, 610 genes were assigned as trait-associated genes. Moreover, 16, 78, and 51 regions of homozygosity (ROHs) were detected in Hanwoo, Black Angus, and Holstein, respectively. ‘Regulation of actin filament length’ was revealed as a significant gene ontology term and 25 trait-associated genes for meat quality and disease resistance were found in 753 genes that resided in the ROHs of Hanwoo. In Hanwoo, 43 genes were located in common ROHs between whole-genome resequencing and SNP chips in BTA2, 10, and 13 coincided with quantitative trait loci for meat fat traits. In addition, the common ROHs in BTA2 and 16 were in agreement between Hanwoo and Black Angus.Conclusions: We identified 4.7 million SNPs and 0.4 million small indels by whole-genome resequencing of a Hanwoo bull. Approximately 25,000 non-synonymous SNPs, splice-site variants, and coding indels (NS/SS/Is) were detected in 8,360 genes. Additionally, we found 25 trait-associated genes for meat quality and disease resistance among 753 genes that resided in the ROHs of Hanwoo. These findings will provide useful genomic information for identifying genes or casual mutations associated with economically important traits in cattle.

Non-synonymous SNPs and protein coding SNPs within the promoter region of genes (regulatory SNPs) might have a significant effect on carcass traits. Imputed sequence level data of 10,215 Hanwoo bulls, annotated and filtered to include only regulatory SNPs (450,062 SNPs), were used in a genome-wide association study (GWAS) to identify loci associated with backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). A total of 15, 176, and 1 SNPs were found to be significantly associated (p < 1.11 × 10⁻⁷) with BFT, CWT, and EMA, respectively. The significant loci were BTA4 (CWT), BTA6 (CWT), BTA14 (CWT and EMA), and BTA19 (BFT). BayesR estimated that 1.1%~1.9% of the SNPs contributed to more than 0.01% of the phenotypic variance. So, the GWAS was complemented by a gene-set enrichment (GSEA) and protein–protein interaction network (PPIN) analysis in identifying the pathways affecting carcass traits. At p < 0.005 (~2,261 SNPs), 25 GO and 18 KEGG categories, including calcium signaling, cell proliferation, and folate biosynthesis, were found to be enriched through GSEA. The PPIN analysis showed enrichment for 81 candidate genes involved in various pathways, including the PI3K-AKT, calcium, and FoxO signaling pathways. Our finding provides insight into the effects of regulatory SNPs on carcass traits.

Marbling (intramuscular fat) is a valuable trait that impacts on meat quality and an important factor determining price of beef in the Korean beef market. Animals that are destined for this high marbling market are fed a high concentrate ration for approximately 30 months in the Korean finishing farms. However, this feeding strategy leads to inefficiencies and excessive fat production. This study aimed to identify candidate genes and pathways associated with intramuscular fat deposition on highly divergent marbling phenotypes in adult Hanwoo cattle.

Background: In this study, we assessed the accuracy of genomic prediction for carcass weight (CWT), marbling score (MS), eye muscle area (EMA) and back fat thickness (BFT) in Hanwoo cattle when using genomic best linear unbiased prediction (GBLUP), weighted GBLUP (wGBLUP), and a BayesR model. For these models, we investigated the potential gain from using pre-selected single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on imputed sequence data and from gene expression information. We used data on 13,717 animals with carcass phenotypes and imputed sequence genotypes that were split in an independent GWAS discovery set of varying size and a remaining set for validation of prediction. Expression data were used from a Hanwoo gene expression experiment based on 45 animals. Results: Using a larger number of animals in the reference set increased the accuracy of genomic prediction whereas a larger independent GWAS discovery dataset improved identification of predictive SNPs. Using pre-selected SNPs from GWAS in GBLUP improved accuracy of prediction by 0.02 for EMA and up to 0.05 for BFT, CWT, and MS, compared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of prediction of BFT and CWT increased when BayesR was applied with the 50 k SNP array (0.02 and 0.03, respectively) and was further improved by combining the 50 k array with the top-SNPs (0.06 and 0.04, respectively). By contrast, using BayesR resulted in limited improvement for EMA and MS. wGBLUP did not improve accuracy but increased prediction bias. Based on the RNA-seq experiment, we identified informative expression quantitative trait loci, which, when used in GBLUP, improved the accuracy of prediction slightly, i.e. between 0.01 and 0.02. SNPs that were located in genes, the expression of which was associated with differences in trait phenotype, did not contribute to a higher prediction accuracy. Conclusions: Our results show that, in Hanwoo beef cattle, when SNPs are pre-selected from GWAS on imputed sequence data, the accuracy of prediction improves only slightly whereas the contribution of SNPs that are selected based on gene expression is not significant. The benefit of statistical models to prioritize selected SNPs for estimating genomic breeding values is trait-specific and depends on the genetic architecture of each trait.