The Genetic Architecture of Carcass and Meat Quality Traits in Beef Cattle

Abstract

<p>This thesis explores the genetic variation of carcass and meat quality traits in beef cattle. Estimation of genetic parameters including heritability, genetic and phenotypic correlations is the first step in the genetic evaluation process to understand the nature of quantitative traits. Subsequently, the estimated parameters are required for the establishing of a selection program in livestock. Alongside with performance data, pedigree information is essential to estimate the relationship between animals more accurately in the conventional breeding program. In beef cattle, carcass traits cannot be recorded on selection candidates and therefore time-consuming progeny tests are often used to gain selection accuracy. However, the discovery of genomic information has enabled to select high merit individuals at an early age without scarifying the selection candidate. Furthermore, the availability of high-density SNP panels and whole genome sequence data has improved the selection accuracy of high merit individuals. Therefore, the general aim of this thesis was to understand the genetic variability of carcass and meat quality traits using pedigree and genomic information in beef cattle.</p> <p>The first experiment of this thesis explored the genetic variation of carcass and meat quality traits in Hanwoo beef cattle using pedigree information. The phenotypic data were collected from 469,002 Hanwoo beef cattle raised at 3646 farms in the Republic of South Korea. The studied carcass traits were carcass weight, eye muscle area, back fat thickness, body weight, and meat index. In addition, the studied meat quality traits included marbling score, meat colour, fat colours and meat texture. Carcass traits, including carcass weight, eye muscle area, back fat thickness and marbling score showed high genetic variation and moderate to high heritability in the Hanwoo beef cattle population. However, the study also revealed that carcass weight and eye muscle area traits showed low and negative (unfavourable) genetic associations with meat texture, meat and fat color traits. Low heritabilities were observed for meat and fat colour traits, however, the observed moderate and positive genetic correlations among meat texture, meat and fat colour traits suggests that these traits can be jointly improved in a breeding program of beef cattle. In this study, the genetic and phenotypic correlations between carcass and meat quality traits were low in general, indicating that these traits are independent and require careful application in selection schemes. In conclusion, the estimates of genetic parameters in this study could be useful for designing breeding programs to improve various carcass and meat quality traits in Hanwoo cattle.</p> <p>The second experiment was designed to examine the drawback of the data obtained from slaughterhouses that were used in the first experiment. Therefore, the second experiment assesses bias due to sorting of animals based on body weight for the genetic evaluation of carcass traits using simulation data. Various sources of bias in genetic evaluation including parental selection, sequential selection, culling of animals before records, and misclassification or manipulation of contemporary groups have been discussed widely in the literature. We hypothesized that the sorting of animals into different contemporary groups based on their yearling weight had an impact on the genetic evaluation of this trait and other correlated traits. The experiment aimed to observe the impact of sorting animals into different contemporary groups based on an early measured trait and then examine the effect on the genetic evaluation of subsequent measured traits. Our result showed that when animals are sorted based on yearling weight leads to biased estimated breeding values in genetic evaluation of carcass traits. The magnitude of the bias in the estimated breeding values that was observed in the current study varied with heritability, and genetic and residual correlations between the simulated traits. The current result demonstrated that the detected sorting biases were stronger when higher genetic and residual correlations were allocated to the simulated traits. However, the observed sorting bias in the univariate model was accounted for by multi-trait evaluation methods. In addition, a slight decrease of bias in estimated breeding values was observed when carcass weight was fitted as a linear covariate in the model for the genetic evaluation of subcutaneous fat depths at the 12th/13th rib (CRIB) trait. Overall, the current simulation study provides insights into how the genetic architecture of studied traits affects the genetic evaluation of animals.</p> <p>The third experiment explored the genetic architecture underlying genetic variability of meat quality traits through the analysis of a genome-wide association study (GWAS) in Hanwoo beef cattle. Genome-wide association studies using common SNP chips have revealed many quantitative trait loci for various production traits such as growth, efficiency, carcass and meat quality traits; however, GWAS using whole genome sequence data are scarce in beef cattle. The current GWAS was conducted using whole-genome sequence data from 2110 Hanwoo beef cattle recorded for marbling score, meat texture, meat and fat color traits. The study identified several chromosomal regions on various chromosomes that contained 107 significant SNPs associated with meat quality traits. Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all studied traits, and their potential influence on the given trait was discussed.</p> <p>The fourth and fifth experiments examined factors that influence the genomic prediction accuracy in beef cattle. Prediction of the breeding values based on information on DNA of the animals is changing breeding strategies and saving time and costs in a breeding program of beef production. The fourth experiment mainly focused on the impact of the relationship between reference and test population was examined on the accuracy of genomic prediction using distantly versus closely related animals in the reference and test populations. The result showed that when the animals in the reference and test populations were closely related, the prediction accuracy was higher than when the animals were related distantly.</p> <p>The fifth experiment assessed the effect of SNP densities including 50K, 777K (HD), whole genome sequence (WGS)) and preselected SNP on the accuracy of genomic prediction. The results showed that similar prediction accuracies were observed across all SNP densities. Small sample size in genomic prediction is a limiting factor to capitalize the benefit of using WGS data since the effect of causal mutations on quantitative traits cannot be accurately estimated. Additionally, high-density markers and WGS data may not help to improve the prediction accuracy in a breed with small effective population size such as Hanwoo beef cattle used in the current study. Depending on the SNP selection methods, zero to 5% improvement of genomic prediction accuracy was gained due to the inclusion of SNPs that were significantly associated with the studied traits, as detected in the GWAS previously. Similarly, different magnitudes of bias were observed in the genomic breeding values depending on the SNP selection methods used in the study. Potential reasons for the observed bias due to the inclusion of preselected SNPs have been discussed and found in other relevant literatures. Overall, the study shows that marbling score and meat texture traits had higher genomic prediction accuracy in all scenarios, suggesting that genomic selection for these traits may contribute well to the genetic improvement of meat quality in Hanwoo beef cattle.</p>