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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.
김보민,Ekanayake Waruni,신정원,김윤식,이두호,김영국,이승환 한국동물유전육종학회 2023 한국동물유전육종학회지 Vol.7 No.4
Improvement of quantitative traits in livestock is an essential goal of animal breeding. For the achievement of this goal, an accurate breeding value estimation is needed. The genetic relationship between reference and test groups is a key factor in determining the accuracy of genomic estimated breeding value (GEBV) thus, the structure of the reference population is crucial for efficient genomic selection. By the number of sharing parents, the population structure can be divided into half-sibling and full-sibling families. Also, the population structure of Hanwoo cattle primarily consists of half-sibling families because of the production system. Therefore, comparing half-sibling and full-sibling families is challenging in the Hanwoo cattle population, yet an important issue in the direction of breeding strategy in Korea. The objective of this study was to compare the accuracy of GEBV between different family structures and investigate efficient family size in the reference population using simulated data. 6 different populations were simulated using QMSim software, and the individuals in the last generations were separated into reference and test groups. The GEBV was calculated using BLUPF90 software. Practical accuracy was between 0.36-0.52 in half-sibling families and 0.55-0.77 in full-sibling families. The increase rate of accuracy was highest at the sibling size of 20, with practical accuracy of 0.52 in half-sibling families and 0.77 in full-sibling families. As a result, the most efficient population structure for genomic prediction was a sibling size of 20 in a full-sibling family.
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.