• The Korean Journal of Malacology
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• v.29 no.4
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• pp.335-341
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• 2013

The seven mitochondrial DNA regions (ND2, ND5, ND4, ND4L, ND6, ND1 and 12SrRNA) of Haliotis discus hannai were examined to estimate the availability as a genetic marker for the study of population genetic. The region with the highest genetic variation was ND4 (Haplotype diversity = 1.0000, Nucleotide diversity = 0.0108). On the other hand, ND2 and ND1 regions have significantly appeared genetic divergence between clusters (divergence of 90% and 87%). Also, pairwise $F_{ST}$ between clusters within ND2 and ND1 regions showed high values; 0.4061 (P = 0.0000), 0.4805 (P = 0.0000) respectively. Therefore we can infer that it is the most efficient and accurate way to analyze the region of ND4 with the highest variation in addition to the regions of ND2 and ND1, which formed clusters with high bootstrap value, for study of population genetic structure in this species.

• Korean Journal of Breeding Science
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• v.40 no.3
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• pp.263-268
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• 2008

Estimates of genetic gain (in volume growth) and diversity (expressed as status number, $N_s$) were determined in a clonal seed orchard of Pinus koraiensis. The genetic thinning was based on clonal breeding values (represented by general combining ability) obtained from progeny tests, clonal fertility estimated by strobilus production, and clonal size variation determined by the ramet numbers per clone. Parental GCA values for volume growth were calculated, based on height and diameter at breast height measured from field trials. Clonal fertility was estimated from the assessments of strobilus production over twelve years from 1991 to 2003, and used for the calculation of status number. There are 179 clones and 5,268 ramets in 12ha area of P. koraiensis clonal seed orchard. Genetic gain and diversity estimates were determined under assumptions of 30% pollen contamination and inferior genetic value of contaminating pollen. Genetic gain increased as thinning rates were set from 10% to 60%. However, for the higher thinning intensities, the increase of genetic gain was not remarkable. Genetic thinning by means of truncation selection resulted in a greater genetic gain but a large decrease in status number. Status number was represented around 40 clones for 10% through 60% thinning intensities, but for the higher thinning intensities, it was a bit fluctuated. Based on the present results, it could be concluded that thinning rate should not be stronger than 60% to optimize genetic gain while conserving genetic diversity. Consequently 50% or 60% thinning rate might be appropriate for genetic thinning in the clonal seed orchard of P. koraiensis. The effect of pollen contamination on the genetic gain and the consequence of genetic thinning for seed production in the clonal seed orchard, and seed orchard management scheme were also discussed.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.737-743
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• 2014

This experiment was conducted to compare heterogeneity for the variance in dairy cattle population and to induce homogeneity of variance using 502,228 performance test records of dairy cattle. The estimates of heritability for milk yields, fat yields and protein yields were 0.28, 0.26 and 0.24, respectively and the estimate of average breeding value by birth year was lower in HV (heterogenous variance) model than in animal model, collectively. The average breeding values of milk yields, fat yields and protein yields for 545 sire bulls applicable to the criteria of interbull MACE programme were 453.54kg, 10.75kg and 14.33kg, respectively and when the heterogeneity was adjusted they were 432.06kg, 10.15kg and 13.40kg, respectively, which were lower in all milk traits collectively. In animal model, coefficients of phenotypic correlation between dataset I and II were 0.839 in milk yields, 0.821 in fat yields, and 0.837 in protein yields, while in HV model, they were 0.841 in milk yields, 0.820 in fat yields, and 0.836 in protein yields, showing similar results in 2 models. When compared using animal model and HV model, the regression coefficient for ratio of number of daughters by calving year of milk yields increased from 15.157 to 16.105 and that of fat yields increased from =0.227 to =0.196, but that of protein yields decreased from 0.630 to 0.586.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.9
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• pp.1214-1218
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• 2004

Data on 958 Karan Fries (KF) and 780 Karan Swiss (KS) cows, born during 1974 to 1992 at National Dairy Research Institute, Karnal were evaluated for causes of culling and their impact on milk performance and herd strength. Causes of culling were classified as voluntary culling (low milk yield) and involuntary culling (other than milk yield). The milk yield of cows was evaluated inretrospectively by estimating expected breeding value (EBV) on the basis of first lactation yield (FLY) and all available lactation yield (ALY). The culling rate of KF cows over the years varied from 10.89 (1988) to 33.92% (1991) with an overall average of 20.96% and in KS from 19.91 (1984) to 33.74% (1989) with an overall average of 25.01%. Reproductive disorders, teat and udder problems, low milk production, health and locomotive disorders were the major reasons of culling accounted respectively for 5.56, 4.97, 4.61, 3.18 and 2.24% of herd strength in KF cows. The corresponding causes of culling were 6.20, 6.26, 7.69, 1.49 and 2.67% of herd strength in KS cows. The involuntary culling of cows accounted for 82.4% in K F and 76.1% in KS cows of total culling. The average annual disposal rate in KF and KS was 26 and 30% whereas annual replacement rate was 24 and 26% respectively. The EBV of involuntary culled cows on the basis of FLY and ALY was 3,111 and 3,515 kg in KF; and 2,669 and 2,940 kg in KS cows respectively. The EBV of selected cows on the basis of FLY and ALY was 3,242 and 3,549 kg in KF and 2,893 and 3,245 kg in KS cows respectively. The average breeding value of involuntary culled cows was not significantly different from selected cows in both the herds. The high rate of involuntary culling of potential cows might be major factor responsible for declined performance and size in these herds. The results indicated that higher genetic gain (2.14% of herd average in KF and 3.49% of herd average in KS) could be obtained by restricting the involuntary culling (50% of total culling) through improved management practices and increasing replacement rate.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.1
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• pp.19-25
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• 2018

Objective: In practical breeding, selection is often performed by ignoring the accuracy of evaluations and applying economic weights directly to the selection index coefficients of genetically standardized traits. The denominator of the standardized component trait of estimated genetic evaluations in practical selection varies with its reliability. Whereas theoretical methods for calculating the selection index coefficients of genetically standardized traits account for this variation, practical selection ignores reliability and assumes that it is equal to unity for each trait. The purpose of this study was to clarify the effects of ignoring the accuracy of the standardized component trait in selection criteria on selection responses and economic weights in retrospect. Methods: Theoretical methods were presented accounting for reliability of estimated genetic evaluations for the selection index composed of genetically standardized traits. Results: Selection responses and economic weights in retrospect resulting from practical selection were greater than those resulting from theoretical selection accounting for reliability when the accuracy of the estimated breeding value (EBV) or genomically enhanced breeding value (GEBV) was lower than those of the other traits in the index, but the opposite occurred when the accuracy of the EBV or GEBV was greater than those of the other traits. This trend was more conspicuous for traits with low economic weights than for those with high weights. Conclusion: Failure of the practical index to account for reliability yielded economic weights in retrospect that differed from those obtained with the theoretical index. Our results indicated that practical indices that ignore reliability delay genetic improvement. Therefore, selection practices need to account for reliability, especially when the reliabilities of the traits included in the index vary widely.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.11
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• pp.1657-1663
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• 2019

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.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.321-336
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• 2018

Sesame (Sesamum indicum L.) is an important oilseed crop grown in tropical and subtropical areas. The objective of this study was to investigate the genetic relationships among 129 sesame landraces and cultivars using simple sequence repeat (SSR) markers. Out of 70 SSRs, 23 were found to be informative and produced 157 alleles. The number of alleles per locus ranged from 3 - 14, whereas polymorphic information content ranged from 0.33 - 0.86. A distance-based phylogenetic analysis revealed two major and six minor clusters. The population structure analysis using a Bayesian model-based program in STRUCTURE 2.3.4 divided 129 sesame accessions into three major populations (K = 3). Based on pairwise comparison estimates, Pop1 was observed to be genetically close to Pop2 with $F_{ST}$ value of 0.15, while Pop2 and Pop3 were genetically closest with $F_{ST}$ value of 0.08. Analysis of molecular variance revealed a high percentage of variability among individuals within populations (85.84%) than among the populations (14.16%). Similarly, a high variance was observed among the individuals within the country of origins (90.45%) than between the countries of origins. The grouping of genotypes in clusters was not related to their geographic origin indicating considerable gene flow among sesame genotypes across the selected geographic regions. The SSR markers used in the present study were able to distinguish closely linked sesame genotypes, thereby showing their usefulness in assessing the potentially important source of genetic variation. These markers can be used for future sesame varietal classification, conservation, and other breeding purposes.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.293-301
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• 2019

This study was done to estimate the effect of progeny numbers and pedigree depth on the accuracy of the estimated breeding value (EBV) using best linear unbiased prediction (BLUP) method in Hanwoo. The experiment groups (sire = 100, 200, and 300; progeny = 4 and 8) were made by random sampling and by genetic evaluation of the following traits: Body weight (BW), carcass weight (CW), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS9). As a result of the genetic evaluation, the accuracy of the EBV was roughly 30 - 60% with 4 progenies, and the accuracy of the EBV increased by about 50 - 75% with 8 progenies. In the other words, when the number of progenies increased from 4 to 8, the accuracy of the EBV simultaneously increased by about 15 - 20%. Moreover, when the number of sires was higher, variations in the accuracy of the EBV within the groups for each trait decreased. Therefore, this result indicates that not only the number of progeny but also the number of sires can affect the accuracy of the EBV. Consequently, collecting information on the progeny and careful management of that information are very important things in the Hanwoo breeding system. Therefore, the EBV can show more precise results when conducting genetic evaluations.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.235-235
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• 2022

Genome selection is a promising tool for plant and animal breeding, which uses genome-wide molecular marker data to capture large and small effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, the prediction accuracy of 10 agricultural traits in the wheat core group with 567 points was compared. We used a cross-validation approach to train and validate prediction accuracy to evaluate the effects of training population size and training model.As for the prediction accuracy according to the model, the prediction accuracy of 0.4 or more was evaluated except for the SVN model among the 6 models (GBLUP, LASSO, BayseA, RKHS, SVN, RF) used in most all traits. For traits such as days to heading and days to maturity, the prediction accuracy was very high, over 0.8. As for the prediction accuracy according to the training group, the prediction accuracy increased as the number of training groups increased in all traits. It was confirmed that the prediction accuracy was different in the training population according to the genetic composition regardless of the number. All training models were verified through 5-fold cross-validation. To verify the prediction ability of the training population of the wheat core collection, we compared the actual phenotype and genomic estimated breeding value using 35 breeding population. In fact, out of 10 individuals with the fastest days to heading, 5 individuals were selected through genomic selection, and 6 individuals were selected through genomic selection out of the 10 individuals with the slowest days to heading. Therefore, we confirmed the possibility of selecting individuals according to traits with only the genotype for a shorter period of time through genomic selection.