Identification of markers associated with estimated breeding value and horn colour in Hungarian Grey cattle

Zsolnai Attila,Kovács András,Kaltenecker Endre,Anton István 아세아·태평양축산학회 2021 Animal Bioscience Vol.34 No.4

Objective: This study was conducted to estimate effect of single nucleotide polymorphisms (SNP) on the estimated breeding value of Hungarian Grey (HG) bulls and to find markers associated with horn colour.Methods: Genotypes 136 HG animals were determined on Geneseek high-density Bovine SNP 150K BeadChip. A multi-locus mixed-model was applied for statistical analyses.Results: Six SNPs were identified to be associated (–log₁₀P>10) with green and white horn. These loci are located on chromosome 1, 3, 9, 18, and 25. Seven loci (on chromosome 1, 3, 6, 9, 10, 28) showed considerable association (–log₁₀P>10) with the estimated breeding value.Conclusion: Analysis provides markers for further research of horn colour and supplies markers to achieve more effective selection work regarding estimated breeding value of HG. Objective: This study was conducted to estimate effect of single nucleotide polymorphisms (SNP) on the estimated breeding value of Hungarian Grey (HG) bulls and to find markers associated with horn colour. Methods: Genotypes 136 HG animals were determined on Geneseek high-density Bovine SNP 150K BeadChip. A multi-locus mixed-model was applied for statistical analyses. Results: Six SNPs were identified to be associated (–log10P>10) with green and white horn. These loci are located on chromosome 1, 3, 9, 18, and 25. Seven loci (on chromosome 1, 3, 6, 9, 10, 28) showed considerable association (–log10P>10) with the estimated breeding value. Conclusion: Analysis provides markers for further research of horn colour and supplies markers to achieve more effective selection work regarding estimated breeding value of HG.

Estimation of Genomic Breeding Value using gBLUP model in ASREML : Practice of ASREML, PLINK, GCTA and R

Yeong Kuk Kim,Doo Ho Lee,Soo Hyun Lee,Ji Min Kang,Seung Hwan Lee 한국동물유전육종학회 2020 한국동물유전육종학회지 Vol.4 No.1

Genomic information is now useful to identify quantitative trait loci (QTL) which is associated with economic traits as well as to predict genetic potential for individual in Animal Breeding Industry. Especially, genomic BLUP (gBLUP) is one of the useful model to estimate genomic breeding value (gEBV) with genomic relationship matrix (GRM). Genomic relationship matrix will be estimated from genomic information such as single nucleotide polymorphism (SNP) which is similar to numeric relationship matrix estimated from pedigree information used in traditional BLUP. The matrix defines the genetic covariance between individuals based on observed similarity using genomic information, rather than on expected genetic similarity from pedigree. Therefore, gBLUP would be given to better prediction accuracy in livestock breeding. 1.GRM (genomic relationship matrix)을 구성할 때 대립유전자의 빈도는 매우 중요하다. 그 이유는 전체적인 matrix의 scale을 조절하기 때문이다. VanRaden이 제안한 최초의 GRM에서는 기초집단의 대립유전자의 빈도를 이용하여 GRM을 구성한다. 그러나, 최근 Forni et al. (2013)에 의하면 base population의 대립유전자의 빈도를 사용하나, 현재 집단의 대립유전자빈도를 사용하여도 추정된 값은 매우 유사하다고 보고한다. 2.ASREM 소프트웨어는 가축개량에서 매우 유용한 프로그램으로 일단 GRM이 만들어 지면 gBLUP모델을 쉽게 설정할수 있는 프로그램이다. 아래의 링크에서 다운로드 할 수 있다.http://www.vsni.co.uk/downloads/asreml 3.ASREML에서 gBLUP을 설정시, GCTA와 같은 소프트웨어를 이용하여 미리 GRM을 구성하여야 한다. 그리고 ASREML에서 육종가 해를 구하기 위하여 inverse를 할 수 있다. GCTA소프트웨어는 아래의 링크에서 다운로드 할 수 있다. http://cnsgenomics.com/software/gcta/#Download

Review on the genetic improvement and application of genomic selection in Korean Hanwoo cattle

Chiemela Peter Nwogwugwu,Yeong Kuk Kim,Ejike Henry Ugbo,이준헌,이승환 한국동물유전육종학회 2020 한국동물유전육종학회지 Vol.4 No.2

Hanwoo cattle (HC) being an indigenous breed are greatly adapted to Korean hot-humid climate. They can survive and thrive in harsh environmental conditions. This makes the HC a valuable genetic resource, given the challenges of climate adjustment and varying demands of the livestock sector. Respects to these genetic attributes of HC, breeding initiatives were designed for genetic improvements, such as the Hanwoo-Gaeryang-Danji (HGD) and Hanwoo-Gaeryang-Nongga (HGN), respectively. These initiatives have resulted in tremendous success in the meat industry. The genetic improvement of HC is somehow fulfilling the breeding objectives of increasing the growth performance traits, enhancing meat quality, improving fertility and maintaining adaptability. The breeding and production systems have also contributed towards achieving the breeding goal. The HC production system comprised of 3-tier, the seed stock, multiplier and feedlot sector. The production system provides a link that enable genetic material from the nucleus herd down to various sectors. The results from various studies on the evaluation of genetic improvement and parameters in Korean HC have revealed the degree of genetic progress. Furthermore, the implementation and the used of pedigree and performance records have been helpful using best linear unbiased prediction (BLUP) to estimate breeding values. In addition, the EBV and accuracy of estimated breeding values (EBVs) are important tool for selecting superior animals to replace the next generation. However, several factors can influence the accuracy of EBVs, such as selection accuracy, selection intensity, pedigree errors and the generation interval (GI). Applying genomic selection (GS) is a potential method to improve prediction accuracy and genetic gains in economically important traits in dairy and beef cattle. Therefore, this study reviews the genetic improvement and application of genomic selection in Korean Hanwoo cattle.

Effect of population structure on the accuracy of genomic prediction for carcass traits in Hanwoo cattle: a simulation study

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.

A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction

이영섭,오재돈,이준영,신동현 한국통합생물학회 2023 Animal cells and systems Vol.27 No.1

Traditionally, the p-value is the criterion for the cutoff threshold to determine significant markers ingenome-wide association studies (GWASs). Choosing the best subset of markers for the best linearunbiased prediction (BLUP) for improved prediction ability (PA) has become an interesting issue. However, when dealing with many traits having the same marker information, the p-values’themselves cannot be used as an obvious solution for having a confidence in GWAS and BLUP. Wethus suggest a genomic estimated breeding value-assisted reduction method of the singlenucleotide polymorphism (SNP) set (GARS) to address these difficulties. GARS is a BLUP-based SNPset decision presentation. The samples were Landrace pigs and the traits used were back fatthickness (BF) and daily weight gain (DWG). The prediction abilities (PAs) for BF and DWG for theentire SNP set were 0.8 and 0.8, respectively. By using the correlation between genomic estimatedbreeding values (GEBVs) and phenotypic values, selecting the cutoff threshold in GWAS and thebest SNP subsets in BLUP was plausible as defined by GARS method. 6,000 SNPs in BF and 4,000SNPs in DWG were considered as adequate thresholds. Gene Ontology (GO) analysis using theGARS results of the BF indicated neuron projection development as the notable GO term, whereasfor the DWG, the main GO terms were nervous system development and cell adhesion.

Lifetime Performance of Nili-ravi Buffaloes in Pakistan

Bashir, M.K.,Khan, M.S.,Bhatti, S.A.,Iqbal, A. Asian Australasian Association of Animal Productio 2007 Animal Bioscience Vol.20 No.5

Data on 1,037 Nili-Ravi buffaloes from four institutional herds were used to study lifetime milk yield, herd life, productive life and breeding efficiency. A general linear model was used to study the environmental effects while an animal model having herd, year of birth and age at first calving (as covariate) along with random animal effect was used to estimate breeding values. The lifetime milk yield, herd life, productive life and breeding efficiency averaged $7,723{\pm}164$ kg, $3,990{\pm}41$ days, $1,061{\pm}19$ days and 64 percent, respectively. All the traits were significantly (p<0.01) affected by the year of birth and herd of calving, while the herd life was also affected (p<0.01) by the age at first calving. The heritabilities for lifetime milk yield, herd life, productive life and breeding efficiency were $0.093{\pm}0.056$, $0.001{\pm}0.055$, $0.144{\pm}0.079$ and 0.001, respectively. The definition for productive life, where each lactation gets credit upto 10 months had slightly better heritability and may be preferred over the definition where no limit is placed on lactation length. The genetic correlation between productive life and lifetime milk yield was low but high between productive life and herd life. The selection for productive life will increase herd life while lifetime milk yield will also improve. The overall phenotypic trend during the period under the study was negative for lifetime milk yield (-280 kg/year), herd life (-93 days), productive life (-42 days/year) and breeding efficiency (-0.36 percent/year), whereas the genetic trend was positive for lifetime milk yield (+15 kg/year) and productive life (+4 days/year).

Comparison of genomic predictions for carcass and reproduction traits in Berkshire, Duroc and Yorkshire populations in Korea

Asif Iqbal,최태정,김유삼,이윤미,M. Zahagir Alam,정종현,최호성,김종주 아세아·태평양축산학회 2019 Animal Bioscience Vol.32 No.11

Objective: A genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms. Methods: The data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs. Results: The accuracies under the GBLUP model for BFT and CWT ranged from 0.33-0.72 and 0.33-0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies. Conclusion: The genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.

전형매 가계 내 유전체정보량에 따른 유전체육종가 추정 및 정확도 분석

김은호,선두원,강호찬,명철현,김지영,이두호,이승환,임현태 경상국립대학교 농업생명과학연구원 2022 농업생명과학연구 Vol.56 No.6

The genomic estimated breeding value (GEBV) and accuracy of Korean beef (Hanwoo) are used as important indicators for selection, and recent studies using pedigree and genotype are being conducted to increase the reliability of the accuracy. Therefore, in this study, three families consisting of 10 Hanwoo with the same parents were collected to check the change in accuracy of individuals without genotypes according to the amount of genotypes in the family. GEBV and accuracy were estimated through single-step genomic best linear unbiased prediction (ssGBLUP) using 14,225 reference groups after assuming 5 test groups by selecting 10, 8, 6, 4, and 2 genotypes for each family. An H-matrix was constructed using pedigree and genotype of the groups, and the GEBV accuracy of carcass weight (CWT), eye muscle area (EMA), backfat thichness (BFT) and marbling score (MS) were estimated using the BLUPF90 program. Looking at the first family, the average GEBV accuracy of 10 heads with a genotype in the test group was estimated to be 0.734, 0.717, 0.712 and 0.745 in CWT, EMA, BFT and MS, respectively. Then, looking at the GEBV accuracy estimated by removing the genotype two by two, there was no change in accuracy in the case of individuals with a genotype, but the accuracy of the individuals without a genotype was estimated to be 0.114~0.168 lower on average. It was confirmed that the average GEBV accuracy of individuals without a genotype was 0.604~0.576, 0.6~0.573, 0.599~0.572 and 0.607~0.578 in CWT, EMA, BFT and MS, respectively. And it was confirmed that they decreased by 0.009 on average. Through this, although the amount of genotypes within a group did not have a significant effect on accuracy regardless of the presence of individual genotypes, the presence of individual genotypes had a greater effect on GEBV with high reliability. 한우 개량에 있어서 추정된 유전체육종가와 정확도는 선발에 중요한 지표로 사용되며, 최근 육종가 추정에 있어 정확도의 신뢰도를 높이기위해 혈통과 유전체정보를 이용한 연구가 활발히 진행되고 있다. 따라서, 본 연구는 가계 내 유전체정보량에 따라 유전체정보가 미포함된 개체의정확도 변화를 확인하고자 동일한 부모를 가진 한우 10두로 구성된 전형매 가계 3개를 수집하였으며, 각 가계 별로 유전체정보량을 10두, 8두, 6두, 4두, 2두씩 무작위로 선별하여 5가지의 검정집단으로 가정한 후 참조집단 14,225두를 이용하여 single step genomic best linear unbiased prediction (ssGBLUP)을 통해 genomic estimated breeding value (GEBV) 및 정확도를 추정하였다. 각 검정집단과 참조집단의 혈통 및 유전체정보를 이용하여 H-matrix를 구축하였고, BLUPF90 program을 사용하여 도체중, 등심단면적, 등지방두께, 근내지방도의 GEBV 및 정확도를 추정하였다. 첫 번째 가계를 대상으로 살펴보면, 검정집단에서 유전체정보를 보유하고 있는 10두의 GEBV 평균 정확도는 도체중 0.734, 등심단면적 0.717, 등지방두께 0.712, 근내지방도 0.745로 추정되었다. 이후 2두씩 무작위로 유전체정보를 제거하여 추정한 GEBV 정확도를 살펴보면, 유전체정보를보유한 개체의 경우 정확도의 변화가 나타나지 않았지만, 유전체정보가 미포함된 개체의 정확도가 평균 0.114 ~ 0.168 낮게 추정되었다. 가계내 유전체정보량에 따른 유전체정보가 미포함된 개체의 GEBV 평균 정확도는 도체중 0.604 ~ 0.576, 등심단면적 0.6 ~ 0.573, 등지방두께 0.599 ~ 0.572, 근내지방도 0.607 ~ 0.578로 평균 0.009씩 감소하는 것을 확인하였다. 이를 통해 가계 내 유전체정보량이 개체 별 유전체정보 유무와는상관없이 GEBV의 정확도 추정에 큰 영향이 없었으며, 신뢰도가 높은 GEBV 추정을 위해서는 개체 별 유전체정보의 유무가 더 큰 영향을 미친다는것을 확인하였다.

Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population

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,이승환,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.

A study of the genomic estimated breeding value and accuracy using genotypes in Hanwoo steer (Korean cattle)

김은호,선두원,강호찬,김지영,명철현,이두호,이승환,임현태 충남대학교 농업과학연구소 2021 Korean Journal of Agricultural Science Vol.48 No.4

The estimated breeding value (EBV) and accuracy of Hanwoo steer (Korean cattle) is an indicator that can predict the slaughter time in the future and carcass performance outcomes. Recently, studies using pedigrees and genotypes are being actively conducted to improve the accuracy of the EBV. In this study, the pedigree and genotype of 46 steers obtained from livestock farm A in Gyeongnam were used for a pedigree best linear unbiased prediction (PBLUP) and a genomic best linear unbiased prediction (GBLUP) to estimate and analyze the breeding value and accuracy of the carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT), and marbling score (MS). PBLUP estimated the EBV and accuracy by constructing a numeric relationship matrix (NRM) from the 46 steers and reference population Ⅰ (545,483 heads) with the pedigree and phenotype. GBLUP estimated genomic EBV (GEBV) and accuracy by constructing a genomic relationship matrix (GRM) from the 46 steers and reference population Ⅱ (16,972 heads) with the genotype and phenotype. As a result, in the order of CWT, EMA, BFT, and MS, the accuracy levels of PBLUP were 0.531, 0.519, 0.524 and 0.530, while the accuracy outcomes of GBLUP were 0.799, 0.779, 0.768, and 0.810. The accuracy estimated by GBLUP was 50.1 - 53.1% higher than that estimated by PBLUP. GEBV estimated with the genotype is expected to show higher accuracy than the EBV calculated using only the pedigree and is thus expected to be used as basic data for genomic selection in the future.