• Animal Bioscience
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• 제36권9호
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• pp.1327-1335
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• 2023

Objective: When evaluating individuals with the same parent and no phenotype by pedigree best linear unbiased prediction (BLUP), it is difficult to explain carcass grade difference and select individuals because they have the same value in pedigree BLUP (PBLUP). However, single step GBLUP (ssGBLUP), which can estimate the breeding value suitable for the individual by adding genotype, is more accurate than the existing method. Methods: The breeding value and accuracy were estimated with pedigree BLUP and ssGBLUP using pedigree and genotype of 408 Hanwoo cattle from 16 families with the same parent among siblings produced by fertilized egg transplantation. A total of 14,225 Hanwoo cattle with pedigree, genotype and phenotype were used as the reference population. PBLUP obtained estimated breeding value (EBV) using the pedigree of the test and reference populations, and ssGBLUP obtained genomic EBV (GEBV) after constructing and H-matrix by integrating the pedigree and genotype of the test and reference populations. Results: For all traits, the accuracy of GEBV using ssGBLUP is 0.18 to 0.20 higher than the accuracy of EBV obtained with PBLUP. Comparison of EBV and GEBV of individuals without phenotype, since the value of EBV is estimated based on expected values of alleles passed down from common ancestors. It does not take Mendelian sampling into consideration, so the EBV of all individuals within the same family is estimated to be the same value. However, GEBV makes estimating true kinship coefficient based on different genotypes of individuals possible, so GEBV that corresponds to each individual is estimated rather than a uniform GEBV for each individual. Conclusion: Since Hanwoo cows bred through embryo transfer have a high possibility of having the same parent, if ssGBLUP after adding genotype is used, estimating true kinship coefficient corresponding to each individual becomes possible, allowing for more accurate estimation of breeding value.

• 한국동물생명공학회지
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• 제38권1호
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• pp.32-37
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• 2023

This study has evaluated the genomic estimated breeding value (GEBV) of the commercial Hanwoo population using the genomic best linear unbiased prediction (GBLUP) method and genomic information. Furthermore, it analyzed the accuracy and realized accuracy of the GEBV. 1,740 heads of the Hanwoo population which were analyzed using a single nucleotide polymorphism (SNP) Chip has selected as the test population. For carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS), the mean GEBVs estimated using the GBLUP method were 3.819, 0.740, -0.248, and 0.041, respectively and the accuracy of each trait was 0.743, 0.728, 0.737, and 0.765, respectively. The accuracy of the breeding value was affected by heritability. The accuracy was estimated to be low in EMA with low heritability and high in MS with high heritability. Realized accuracy values of 0.522, 0.404, 0.444, and 0.539 for CWT, EMA, BFT, and MS, respectively, showing the same pattern as the accuracy value. The results of this study suggest that the breeding value of each individual can be estimated with higher accuracy by estimating the GEBV using the genomic information of 18,499 reference populations. If this method is used and applied to individual selection in a commercial Hanwoo population, more precise and economical individual selection is possible. In addition, continuous verification of the GBLUP model and establishment of a reference population suitable for commercial Hanwoo populations in Korea will enable a more accurate evaluation of individuals.

• Journal of Animal Science and Technology
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• 제65권4호
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• pp.720-734
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• 2023

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 (M_e) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between M_e and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between M_e 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 M_e, the higher the accuracy of EBV.

• 생명과학회지
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• 제32권4호
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• pp.279-284
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• 2022

본 연구는 일반농가에서 적용 가능한 유전평가시스템을 구축을 위해 경기 지역에서 사육중인 암소 619두를 BLUP (Best Linear Unbiased Prediction)과 GBLUP (Genomic Best Linear Unbiased Prediction)을 사용하여 각 형질(도체중, 등심단면적, 등지방두께, 근내지방도) 별 추정 육종가의 정확도를 비교분석 하였다. GBLUP의 경우 참조집단의 크기를 다르게 그룹을 나누어 분석하였다. 분석결과 GBLUP 참조집단의 크기가 커질수록 각 형질의 육종가의 정확도도 상승하는 것을 확인 하였다. BLUP과 GBLUP 방법을 사용하여 추정한 육종가의 정확도를 비교하면, GBLUP 방법을 사용하여 육종가를 추정하였을 때 도체중, 등심단면적, 등지방두께 근내지방도순으로 각각 0.10, 0.09, 0.09, 0.11 이상 상승한 것을 확인할 수 있었다. 따라서, GBLUP 방법을 암소 평가 및 선발에 적용한다면, 정밀하고 정확한 개체 선발이 가능하고 참조집단의 크기를 더욱 키운다면 보다 정확한 개체 선발을 할 수 있기 때문에 선발의 효율성이 증가할 것으로 사료된다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권3_4호
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• pp.379-387
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• 2003

다수의 미생물 유전체 프로젝트들이 완료되면서 엄청난 양의 유전체 핵산 염기서열 데이터들이 양산되고 있다. 이러한 상황에서 전산 기법을 이용하여 유전체 DNA 염기서열 상에서 유전자의 프로모터 영역을 규명하는 문제는 최근에 상당한 연구의 관심대상으로 떠오르고 있다. 본 논문에서는 전사조절의 핵심 역할을 하는 -10 영역과 전사개시 부위를 포함한 원핵생물의 핵심 프로모터 영역에 대한 의존성 반영 분해모델 (Dependency-Reflecting Decomposition Model)을 제안한다. 이 모델은 인접한 위치에 존재하는 핵산 염기들 사이의 의존성뿐만 아니라 인접하지 않은 위치의 핵산 염기들간의 의존성까지 고려함으로써 핵산 염기서열 상에 내포되어있는 중요한 생물학적 의존성들을 함축하고 있다. DRDM 모델은 우수한 성능평가 결과를 보였으며. 미생물 유전체 Contig들 상에서 임의의 유전자 프로모터를 예측하는데 효과적으로 이용될 수 있다.

• Asian-Australasian Journal of Animal Sciences
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• 제27권12호
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• pp.1678-1683
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• 2014

This study considers a problem of genomic selection (GS) for adjacent genetic markers of Yorkshire pigs which are typically correlated. The GS has been widely used to efficiently estimate target variables such as molecular breeding values using markers across the entire genome. Recently, GS has been applied to animals as well as plants, especially to pigs. For efficient selection of variables with specific traits in pig breeding, it is required that any such variable selection retains some properties: i) it produces a simple model by identifying insignificant variables; ii) it improves the accuracy of the prediction of future data; and iii) it is feasible to handle high-dimensional data in which the number of variables is larger than the number of observations. In this paper, we applied several variable selection methods including least absolute shrinkage and selection operator (LASSO), fused LASSO and elastic net to data with 47K single nucleotide polymorphisms and litter size for 519 observed sows. Based on experiments, we observed that the fused LASSO outperforms other approaches.

• Animal Bioscience
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• 제35권5호
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• pp.659-669
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• 2022

Objective: Thoroughbred horses have been bred exclusively for racing in England for a long time. Additionally, because horse racing is a global sport, a healthy leisure activity for ordinary citizens, and a high-value business, systematic racehorse breeding at the population level is a requirement for continuous industrial development. Therefore, we established genomic evaluation system (using prize money as horse racing traits) to produce spirited, agile, and strong racing horse population Methods: We used phenotypic data from 25,061 Thoroughbred horses (all registered individuals in Korea) that competed in races between 1994 and 2019 at the Korea Racing Authority and constructed pedigree structures. We quantified the improvement in racehorse breeding output by year in Korea, and this aided in the establishment of a high-level horse-fill industry. Results: We found that pedigree-based best linear unbiased prediction method improved the racing performance of the Thoroughbred population with high accuracy, making it possible to construct an excellent Thoroughbred racehorse population in Korea. Conclusion: This study could be used to develop an efficient breeding program at the population level for Korean Thoroughbred racehorse populations as well as others.

• Animal Bioscience
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• 제37권3호
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• pp.451-460
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• 2024

Objective: The objective is to identify genomic regions and candidate genes associated with age to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) in Yorkshire pig. Methods: This study used a total of 104,380 records and 11,854 single nucleotide polymorphism (SNP) data obtained from Illumina porcine 60K chip. The estimated genomic breeding values (GEBVs) and SNP effects were estimated by single-step genomic best linear unbiased prediction (ssGBLUP). Results: The heritabilities of AGE, ADG, BF, and EMA were 0.50, 0.49, 0.49, and 0.23, respectively. We identified significant SNP markers surpassing the Bonferroni correction threshold (1.68×10^-6), with a total of 9 markers associated with both AGE and ADG, and 4 markers associated with BF and EMA. Genome-wide association study (GWAS) analyses revealed notable chromosomal regions linked to AGE and ADG on Sus scrofa chromosome (SSC) 1, 6, 8, and 16; BF on SSC 2, 5, and 8; and EMA on SSC 1. Additionally, we observed strong linkage disequilibrium on SSC 1. Finally, we performed enrichment analyses using gene ontology and Kyoto encyclopedia of genes and genomes (KEGG), which revealed significant enrichments in eight biological processes, one cellular component, one molecular function, and one KEGG pathway. Conclusion: The identified SNP markers for productive traits are expected to provide valuable information for genetic improvement as an understanding of their expression.

• Asian-Australasian Journal of Animal Sciences
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• 제33권10호
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• pp.1544-1557
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• 2020

Objective: Genomic selection (GS) is becoming popular in animals' genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method. Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson's correlation of genomic estimated breeding value (GEBV) with EBV of all animals (r_M1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (r_M2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls). Results: The r_M1 estimates in the study were between 0.90 and 0.96 among five traits. The r_M1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average r_M2 estimates were much smaller than r_M1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, r_M2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of r_M2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the r_M2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based r_M2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%). Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo proven-bull evaluation program.

• Genomics & Informatics
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• 제9권4호
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• pp.194-196
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• 2011

Promoter prediction is a very important problem and is closely related to the main problems of bioinformatics such as the construction of gene regulatory networks and gene function annotation. In this context, we developed an integrated promoter prediction program using hybrid methods, PromoterWizard, which can be employed to detect the core promoter region and the transcription start site (TSS) in vertebrate genomic DNA sequences, an issue of obvious importance for genome annotation efforts. PromoterWizard consists of three main modules and two auxiliary modules. The three main modules include CDRM (Composite Dependency Reflecting Model) module, SVM (Support Vector Machine) module, and ICM (Interpolated Context Model) module. The two auxiliary modules are CpG Island Detector and GCPlot that may contribute to improving the predictive accuracy of the three main modules and facilitating human curator to decide on the final annotation.