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RISS 인기검색어
- 검색키워드
- 저자 : Phuong Thanh N. Dinh (검색결과 4 건)
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Association Study of Functional Candidate Genes for Meat Tenderness in Hanwoo Cattle
Phuong Thanh N. Dinh,Yoonji Chung,Dong Jae Lee,Dong-Hun Kang,Bo Hye Park,Ki-Yong Chung,이승환 한국동물유전육종학회 2021 한국동물유전육종학회지 Vol.5 No.4
Hanwoo (Korean cattle) is the most profitable cattle as meat-type one since 1960s with the cherished role in the food industry due to its palatability, which is performed by several characteristics such as tenderness, juiciness, or flavor. Meat tenderness is one of the most important features for the quality of meat and is affected subjectively and objectively by several factors. Therefore, it is essential to identify the distribution of potentially functional SNPs or candidate regions associated with tenderness. Starting from 12 candidate genes extracted from previous study, including CNOT3, OSCAR, NLRP5, PIBF1, ITBG3, COL11A1, COL1A, COL24A1, COL28A, COL2A1, COL4A3 and COL6A3, an association study for tenderness in 1161 Hanwoo beef cattle was performed using linear mixed model through MLMA module of GCTA software, fitting a genetic relationship matrix built from 11,227,701 SNPs of the whole genome and 7 environmental factors to predict the effects of 730 candidate SNPs. SNP that fitted to the model having the p-value < 0.01 will be in a significant association with the trait. 10 SNPs and 7 SNPs related to tenderness of semimembranosus area (S_SF) and longissimus dorsi area (D_SF) were detected, respectively. Most of them are phenotypic variations of collagen family genes, including: COL1A1, COL4A3, COL6A3 and COL11A1. This result suggests an attempt to explore effective biomarkers for meat tenderness trait in Hanwoo cattle.
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Ekanayake Waruni,Dinh Phuong Thanh N.,이준헌,이승환 한국동물유전육종학회 2024 한국동물유전육종학회지 Vol.8 No.1
The present study deploys a comparison of Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Genome Wide Association Studies (GWAS) in selecting optimum subsets of single nucleotide polymorphisms (SNPs) to be used in genomic prediction in cattle. The data simulation was carried out for 6,000 animals and 47,841 SNPs which include 43,633 polygenic markers and 4208 quantitative trait loci (QTL) using QMSim software. The genomic prediction was conducted with the best linear unbiased prediction (BLUP) method using the BLUPF90 program. The accuracy of prediction was computed in three different types, namely, Empirical all SNPs, Empirical QTL, and theoretical accuracy, Accuracy PEV . Among the three models, the highest Empirical all SNPs accuracy 0.79 was derived for GBM followed by 0.77 for XGBoost and 0.76 for GWAS. The Empirical QTL accuracy was almost equal for all three models. The maximum theoretical accuracy was obtained for GWAS which was 0.93, whereas GBM and XGBoost obtained 0.86 and 0.85 accuracy levels respectively. Our results indicate that all three models comparably performed in genomic predictions; however, subsets selected by both GBM and GWAS reported higher prediction accuracies compared to the whole SNP set. The number of QTL selected as a proportion of the total number of SNPs was superior in GWAS. These observations can be validated using real data which could enable further optimization of the analysis process.
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Hyo Sang Lee,Yeong Kuk Kim,Doo Ho Lee,Dongwon Seo,Dong Jae Lee,ChangHee Do,Phuong Thanh N. Dinh,Waruni Ekanayake,Kil Hwan Lee,Du-Hak Yoon,Seung Hwan Lee,Yang Mo Koo 한국축산학회 2023 한국축산학회지 Vol.65 No.4
In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (Me) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between Me and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between Me and accuracy were −0.74 in CWT, −0.75 in EMA, −0.73 in MS, and −0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the Me, the higher the accuracy of EBV.
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