• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.289-296
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• 2011

Swine industry in Korea plays an important role in providing the meat for domestic consumption, and the number of pigs in Korea was about 9.72 million heads as of June, 2010. Meat quality is used to describe any traits which impact the consumer acceptability of fresh meat products. Meat color, firmness, water holding capacity, ultimate muscle $pH_{24h}$ (measured 24 hours post-mortem), shear force, and intramuscular fat percentage (IMF) are generally accepted as important indicators of meat quality and ultimately, consumer acceptance of fresh pork. The objective of this study was to estimate genetic parameters for meat quality traits in Berkshire pigs. The heritability estimates for muscle $pH_{24h}$, lightness (CIE $L^*$), NPPC marbling were 0.61, 0.56 and 0.57, respectively, The heritability estimates for drip loss, cooking loss, shear force were 0.51, 0.66 and 0.56, respectively. The phenotypic correlations between $pH_{24h}$ and lightness (CIE $L^*$), drip loss, cooking loss were negative, ranging from -.45 ~ -.13. The genetic correlations between muscle $pH_{24h}$ and lightness (CIE $L^*$), drip loss were negative, ranging from -.35 ~ -.32. Genetic parameters obtained herein indicate that genetic improvement of muscle $pH_{24h}$ is not related to the NPPC marbling of meat, but rather to improved lightness(CIE $L^*$) and drip loss. Genetic trends of meat quality traits showed increased muscle $pH_{24h}$ and decreased cooking loss and drip loss.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1249-1258
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• 2015

DGAT1(diacylglycerol O-acyltransferase1) gene is well known as a major gene of milk production in dairy cattle. This study was conducted to investigate how the DGAT1 gene effect on milk yield was appeared from the genome wide association (GWA) using high density whole genome SNP chip. The data set used in this study consisted of 353 Korean Holstein sires with 50k SNP genotypes and deregressed estimated breeding values of milk yield. After quality control 41,051 SNPs were selected and locations on chromosome were mapped using UMD 3.1. Bayesian regression of BayesB method (pi=0.99) was used to estimate the SNP effects and genomic breeding values. Percentages of variance explained by 1 Mb non-overlapping windows were calculated to detect the QTL region. As the result of this study, top 1 and 3 of 2,516 windows were seen around DGAT1 gene region and 0.51% and 0.48% of genetic variance were explained by these two windows. Although SNPs on the DGAT1 gene region are excluded in commercial 50k SNP chip, the effect of DGAT1 gene seem to be reflected on GWA by the SNPs which are in linkage disequilibrium with DGAT1 gene.

• Journal of agriculture & life science
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• v.44 no.2
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• pp.29-38
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• 2010

This study looked into estimation of variance component over swine's economic traits by multiple animal model and maternal effect model using on-farm test data of total 31,455 swine of Duroc, Landrace and Yorkshire species that were born between 2000 and 2008. Heritability by estimated additive genetic effect showed higher than one by maternal genetic effect using multivariate maternal animal model in each trait examined by each breed and most heritability when considering only additive genetic effect in multiple traits animal model was estimated to be higher than one by estimated additive genetic effect in multivariate maternal animal model. In correlation between breeding value by estimated maternal genetic effect and phenotypic value using multivariate maternal animal model, rank correlation and simple correlation of breeding value and phenotypic value by maternal genetic effect also showed low positive correlation or strong negative correlation, which can be considered that if correlation with phenotype were increased properly considering maternal genetic effect in each trait by each breed, even better improvement could be promoted.

• Journal of Korean Society of Forest Science
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• v.70 no.1
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• pp.109-114
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• 1985

Utilization of $F_2$ seeds in forest tree hybridization program could be as dangerous as it could turn out to be handy and beneficial. Variation in $F_2$ generation does not appear to be dangerous as many breeder's concern. Pitch-loblolly pine hybridization program in Korea is reviewed and use of $F_2$ seeds are strongly recommended under the strict guide line of further breeding program. Breeding strategy for pitch-loblolly pine hybridization is suggested.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.42-45
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• 2003

작물의 생산성은 재배작물의 품종이 갖는 유전적 특성과 재배기술 및 재배환경에 의해 결정되는 것으로, 이중 품종과 기술은 육종가와 재배기술자의 노력으로 많은 발전을 가져오고 있지만, 재배환경은 인위적으로 조절할 수 없는 부분이 많고 특히 기상환경은 자연기상조건에 의존하고 있다. 그러므로, 특정 지역에서 재배하는 작물과 품종은 그 지방 기후에 적응되는 과정을 거쳐 그 지역의 풍토에 알맞게 진화되어 왔다고 볼 수 있다.(중략)

• Korean Journal of Breeding Science
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• v.42 no.5
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• pp.495-501
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• 2010

Peaches (Prunus persica) are less popular than the fresh fruits, because their flesh gets soft faster. So many breeders focused on their aim to firmness. Other breeders focused on juiciness, flavor and aroma. Breeding requires much labor, time and money. To reduce these requirements, many scientists develop many SSR, CAPS and SCAR makers. New peach varieties bred in our National Institute of Horticultural & Herbal Science (NIHHS) such as, Cheonhong, Suhong and Harhong are yellow flesh cultivars and Yumyeong, Baekmijosaeng, Baekhyang, Jinmi, Soomee, Mihong, Misshong and Yumee are white flesh cultivars. These peach cultivars are planted in orchard of Korea. To assert breeding cultivar patents and prevent patent disputes, we detected cultivar-specific DNA fragment using 235 sets of Operon RAPD primers, analyzed 134 DNA sequences and constructed SCAR primers. To confirm the cultivar-specific SCAR markers, we applied candidate SCAR primers to 30 peach cultivars widely cultivated in Korea. These selected lines are included father and mother lines that were used to develop new varieties in NIHHS. Using fourteen SCAR primer sets, we characterized thirty cultivars selected. The SCAR marker is expected to serve as molecular evidence distinguishing different peach varieties.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.429-435
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• 2016

Heritability estimates for daily gain (g), backfat thickness (mm), days to 90kg (day), loin eye depth (mm) and meat percent (%) were 0.40, 0.44, 0.40, 0.25 and 0.48, respectively. Estimates of correlation between breeding value and rank for meat productivity traits by model 1 and 2 were 0.995 1.000 and 0.991 1.000, respectively and highly significant (p< 0.0001), and they were almost identical to the breeding values estimated by different farms. When top 5% and top 10% animals were selected by meat productive traits at different farms, markedly different animals were selected by farms since the selected improvement traits in each farm maintaining closed herds were different. Therefore, it seems to be desirable that superior pigs should be selected after the establishment of evaluation system for genetic performance at national level using the integrated data obtained from various farms.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.359-365
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• 2013

Estimate breeding value can be used as single trait animal model was developed directly using the Fortran language program. The program is based on data computed by using the indirect method repeatedly. The program develops a common algorithm and imprves efficiency. Algorithm efficiency was compared between the two programs. Estimated using the solution is easy to farm and brand the service, pedigree data base was associated with the development of an improved system. The existing program that uses the single trait animal model and the comparative analysis of efficiency is weak because the estimation of the solution and the conventional algorithm programmed through regular formulation involve many repetition; therefore, the newly developed algorithm was conducted to improve speed by reducing the repetition. Single trait animal model was used to analyze Gauss-Seidel iteration method, and the aforesaid two algorithms were compared thorough the mixed model equation which is used the most commonly in estimating the current breeding value by applying the procedures such as the preparation of information necessary for modelling, removal of duplicative data, verifying the parent information of based population in the pedigree data, and assigning sequential numbers, etc. The existing conventional algorithm is the method for reading and recording the data by utilizing the successive repetitive sentences, while new algorithm is the method for directly generating the left hand side for estimation based on effect. Two programs were developed to ensure the accurate evaluation. BLUPF90 and MTDFREML were compared using the estimated solution. In relation to the pearson and spearman correlation, the estimated breeding value correlation coefficients were highest among all traits over 99.5%. Depending on the breeding value of the high correlation in Model I and Model II, accurate evaluation can be found. The number of iteration to convergence was 2,568 in Model I and 1,038 in Model II. The speed of solving was 256.008 seconds in Model I and 235.729 seconds in Model II. Model II had a speed of approximately 10% more than Model I. Therefore, it is considered to be much more effective to analyze large data through the improved algorithm than the existing method. If the corresponding program is systemized and utilized for the consulting of farm and industrial services, it would make contribution to the early selection of individual, shorten the generation, and cultivation of superior groups, and help develop the Hanwoo industry further through the improvement of breeding value based enhancement, ultimately paving the way for the country to evolve into an advanced livestock country.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.13-18
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• 2013

This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~0.957. In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.1-6
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• 2013

This study was aimed to solve the problems of current national genetic evaluation systems in Korea and its development to pass the verification processes as required by International Bull Evaluation Service (Interbull). This will enable Korea to participate in international genetic evaluation program. A total of 1,416,589 test-day milk records with calving dates used in this study were collected by National Agricultural Cooperative Federation from 2001 to 2009. Parity was limited up to fifth calving and milk production records were adjusted to cumulative 305 day lactation. The pedigree consisted of 2,279,741 animals where 2,467 bulls had 535,409 parents. A newly developed multiple trait model was used in calculation of breeding values for milk yield, milk fat, and protein yield. Data were edited with SAS (version 9.2) and R programs, and genetic parameters were estimated using VCE 6.0. Results showed a continuous increase in genetic potentials, in general, and no remarkable differences were found between performances by parity. Except fat yield, potentials in milk yield and protein yield were well calculated. We found an increased number of daughters per each top ranked 1,000 bulls in recent years of calf births compared to the cases of previous evaluations. Of the bulls ranked top 100 by our new models (multiple-trait models) we found that increased numbers of bulls were included. Of twenty eight bulls born in 2006, twenty bulls born in 2007 and eight bulls born in 2008 that were listed by new models, only 23, 12, and 2 bulls born in respective years were represented on top 100 by old single-trait models. Re-ranking of the daughters or sires by multiple-trait models suggest that this new multiple trait approach should be used for dairy cattle genetic evaluation and seed-stock selection in the future to increase the accuracy of multiple trait selection. Breeding values for these traits should also be calculated by new method for international genetic evaluation.