Use of Genomics to Improve the Genetic Merit in Hanwoo Beef Cattle

Abstract

<p>The advent of genomic technologies, and its use in the livestock industry has accelerated the increase of genetic merit in multiple breeds. The Hanwoo cattle breed, known for its high-marbled meat, stand to benefits from the integration of genomic information into the breeding system. This thesis aims to explore the potential of utilizing genomic information to enhance the genetic merit of four carcase traits in the Hanwoo breed by exploring the benefits of using pre-selected SNPs and the implementation of female selection in breeding programs.</p> <p>In Chapter 1 of this thesis, we provide a comprehensive introduction to the overarching research topic. In Chapter 2 a thorough review of relevant literature is presented, setting the stage for a deeper understanding of the current research landscape. Chapter 3 of this research focused on conducting a genome-wide association study (GWAS) for four carcase traits using genotyped animals with relatives’ phenotypic information in Hanwoo. GWAS is a method used to identify genomic regions associated with the phenotypic information, and it has previously been applied to Hanwoo cattle. However, a limitation of these previous studies was the small size of the data set. Although there is a large amount of phenotypic information from carcase recording system in Hanwoo, genotyping of those animals has been restricted due to cost constraints. To overcome this limitation and make use of the available phenotypic information, we utilized a dataset comprising 13,715 animals, which have both genotype and phenotypes, and 454 genotyped sires with progeny phenotype information from 440,284 progeny for this study. There were 33 QTLs and 313 candidate genes identified for carcase weight, as well as 17 QTLs and 122 candidate genes for back fat thickness. Additionally, we detected 19 QTLs and 137 genes for eye muscle area and 12 QTLs and 77 candidate genes for marbling score. This approach has identified additional genomic regions and candidate genes associated with the carcase traits in Hanwoo cattle. Furthermore, these findings demonstrate the potential to increase the power of GWAS by including a large number of animals with phenotypes that are related to genotyped animals. </p> <p>The identification of selection signatures is a technique employed to reveal the influence of natural or artificial selection in a population. This approach is widely used to identify genomic regions potentially linked to economically important traits. In Chapter 4, we conducted a comprehensive assessment of selection signatures employing three methods. Our analysis encompassed both within and between population evaluations, focusing on the Hanwoo and Angus populations using whole-genome sequence data. Within the Hanwoo population, we identified a total of 374 significant genomic regions, while the Angus population, exhibited selection signatures in 65 genomic regions. When comparing between the two populations, we found 70 genomic regions indicating divergent selection. The candidate genes related with the meat quality traits like <i>HSPA9</i> and <i>LPL</i> were identified within Hanwoo. Genes associated with the meat quantity and growth traits (<i>ACTC1</i> and <i>TMEM68</i>) were detected within Angus, and between Hanwoo and Angus. This study showed the selection history of each breed and provided insights into the genomic regions under selection. </p> <p>In Chapter 5, we assessed the use of pre-selected SNPs from both GWAS and signature of selection analysis to improve the prediction accuracy of carcase traits in Hanwoo cattle. Since the pre-selected SNPs could have the potential to increase the prediction accuracy by explaining a large proportion of the phenotypic variance, the pre-selected SNPs were detected using common and widely used methods, as previously described in Chapters 3 and 4. We conducted a comparison involving BayesR and Genomic Best Linear Unbiased Prediction (GBLUP), the latter using the genomic relationship matrix (GRM), which was fitted with a standard 50K SNP array, augmented with pre-selected SNPs from GWAS, SoS and combined (GWAS + SoS) SNP sets. The result indicated that combining the pre-selected SNPs from both GWAS and signature of selection analysis yield the highest increase in prediction accuracy by 15.1% for carcase weight and by 12.3% back fat thickness using the GBLUP model with two GRMs fitted. For eye muscle area and marbling score, the highest prediction accuracy increase by up to 13.5% was achieved when using only the pre-selected SNPs from GWAS, without incorporating additional SNPs from the signature of selection analysis. By identifying informative genomic regions, we can potentially achieve higher prediction accuracy for these traits. The utilization of pre-selected SNPs from GWAS offers advantages for improving the prediction accuracy of carcase traits in Hanwoo. Expanding the dataset for GWAS emerges as a necessity, enabling the identification of a greater number of genomic regions for traits like MS, which currently exhibits comparatively lower accuracy than its counterparts. Furthermore, it might be useful to expand studies into the signatures of selection analysis to more inter-breed comparison to identify additional SNPs associated with carcase traits. These approaches promises to unveil a broader spectrum of genetic markers for carcase traits.</p> <p>The Hanwoo breeding program traditionally focused on male selection for carcase traits based on the progeny records, as these traits are difficult to measure directly on selection candidates. With the development genomic information, selection of progeny tested males can be replaced by selection on younger males based on the genomic test. Selecting females with genomic information could be of benefit as well, to increase prediction accuracy and reduced generation interval in the breeding nucleus, however this practice has not been incorporated into the Hanwoo breeding system. Chapter 6 of this thesis comprises a simulation study to assess potential genetic gain in selecting genotyped females and how this is influenced by factors like prediction accuracy, selection intensity, and generation interval. Both deterministic and stochastic simulation models were employed. The results demonstrated that implementing female selection using genomic information provided a predicted improvement in genetic gain by 5.9% and 10.8% in the deterministic and stochastic simulations, respectively. Prediction accuracy of female candidates emerged as the primary factor driving this genetic gain, and the use of genomic selection helped to increase this accuracy. These findings imply that there is a considerable benefit of implementing female selection with genomic information in the Hanwoo breeding system. Moreover, this approach can be extended to include not only carcase traits but also other traits, such as reproduction and growth traits.</p> <p>The final chapter offers a discussion of the research and concludes with recommendations for future research directions. These recommendations encompass areas like prediction accuracy enhancement and novel breeding programs, aiming to further enhance the genetic improvement of Hanwoo. The thesis serves as a significant contribution to the understanding of Hanwoo breeding and paves the way for more efficient and effective breeding strategies in the future.</p>