• Journal of Animal Science and Technology
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• v.52 no.5
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• pp.357-366
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• 2010

The emergence of next-generation sequencing technologies has lead to application of new computational and statistical methodologies that allow incorporating genetic information from entire genomes of many individuals composing the population. For example, using single-nucleotide polymorphisms (SNP) obtained from whole genome amplification platforms such as the Ilummina BovineSNP50 chip, many researchers are actively engaged in the genetic evaluation of cattle livestock using whole genome relationship analyses. In this study, we estimated the genomic relationship matrix (GRM) and compared it with one computed using a pedigree relationship matrix (PRM) using a population of Hanwoo. This project is a preliminary study that will eventually include future work on genomic selection and prediction. Data used in this study were obtained from 187 blood samples consisting of the progeny of 20 young bulls collected after parentage testing from the Hanwoo improvement center, National Agriculture Cooperative Federation as well as 103 blood samples from the progeny of 12 proven bulls collected from farms around the Kyong-buk area in South Korea. The data set was divided into two cases for analysis. In the first case missing genotypes were included. In the second case missing genotypes were excluded. The effect of missing genotypes on the accuracy of genomic relationship estimation was investigated. Estimation of relationships using genomic information was also carried out chromosome by chromosome for whole genomic SNP markers based on the regression method using allele frequencies across loci. The average correlation coefficient and standard deviation between relationships using pedigree information and chromosomal genomic information using data which was verified using a parentage test andeliminated missing genotypes was $0.81{\pm}0.04$ and their correlation coefficient when using whole genomic information was 0.98, which was higher. Variation in relationships between non-inbred half sibs was $0.22{\pm}0.17$ on chromosomal and $0.22{\pm}0.04$ on whole genomic SNP markers. The variations were larger and unusual values were observed when non-parentage test data were included. So, relationship matrix by genomic information can be useful for genetic evaluation of animal breeding.

• Proceedings of the KSAR Conference
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• 2003.06a
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• pp.55-55
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• 2003

The random insertion of useful gene in genome has been a common method to produce transgenic animals. This method is inefficient for induction of high levels gene expression in transgenic animals. To improve this limit, we tried to develop the system which target the gene at the specific genomic region. Thus, in our experiment, the vector system to target the human thrombopoietin (TPO) gene was developed. Targeting vector including TPO, neo and DT genes was transfrcted into bovine embryonic fibroblasts (bEF) or bovine ear skin fibroblasts (bESF). First of all, we determined concentration of the geneticin (G418) for selection of transfected cell lines. Our results showed that 1200 and 900 $\mu\textrm{g}$/ml of G418 were the most proper for selection of transfscted bEF and bESF cells. In this study, lipofectamine was used as a transfection reagent. Thus, the proper ratio of DNA:lipofectamine for transfection was also required to elevate targeting efficiency in primary mammalian cells. Our result indicates that the most proper ratios of DNA:lipofectamine were 4:2 and 1:2 in bEF and bESF cells. According to the optimized these conditions, single colonies were picked following transfection and were analyzed by PCR. More than 90% of the single colonies have TPO gene. However, there were no colonies with targeted TPO at the specific genomic region. Therefore, further experiments to select the specifically targeted colonies and to find more efficient methods such as reducing selection time and shortening a size of TPO gene are required.

• Animal Bioscience
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• v.37 no.8
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• pp.1355-1366
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• 2024

Objective: The analysis of runs of homozygosity (ROH) has been applied to assess the level of inbreeding and identify selection signatures in various livestock species. The objectives of this study were to characterize the ROH pattern, estimate the rate of inbreeding, and identify signatures of selection in the red-brown Korean native chickens. Methods: The Illumina 60K single nucleotide polymorphism chip data of 651 chickens was used in the analysis. Runs of homozygosity were analysed using the PLINK v1.9 software. Inbreeding coefficients were estimated using the GCTA software and their correlations were examined. Genomic regions with high levels of ROH were explored to identify selection signatures. Results: A total of 32,176 ROH segments were detected in this study. The majority of the ROH segments were shorter than 4 Mb. The average ROH inbreeding coefficients (F_ROH) varied with the length of ROH segments. The means of inbreeding coefficients calculated from different methods were also variable. The correlations between different inbreeding coefficients were positive and highly variable (r = 0.18-1). Five ROH islands harbouring important quantitative trait loci were identified. Conclusion: This study assessed the level of inbreeding and patterns of homozygosity in Red-brown native Korean chickens. The results of this study suggest that the level of recent inbreeding is low which indicates substantial progress in the conservation of red-brown Korean native chickens. Additionally, Candidate genomic regions associated with important production traits were detected in homozygous regions.

• BMB Reports
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• v.51 no.1
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• pp.1-2
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• 2018

Positive selection on a new beneficial mutation generates a characteristic pattern of DNA sequence polymorphism when it reaches an intermediate allele frequency. On genome sequences of African Drosophila melanogaster, we detected such signatures of selection at 37 candidate loci and identified "sweeping haplotypes (SHs)" that are increasing or have increased rapidly in frequency due to hitchhiking. Based on geographic distribution of SH frequencies, we could infer whether selective sweeps occurred starting from de novo beneficial mutants under simple constant selective pressure. Single SHs were identified at more than half of loci. However, at many other loci, we observed multiple independent SHs, implying soft selective sweeps due to a high beneficial mutation rate or parallel evolution across space. Interestingly, SH frequencies were intermediate across multiple populations at about a quarter of the loci despite relatively low migration rates inferred between African populations. This invokes a certain form of frequency-dependent selection such as heterozygote advantage. At one locus, we observed a complex pattern of multiple independent that was compatible with recurrent frequency-dependent positive selection on new variants. In conclusion, genomic patterns of positive selection are very diverse, with equal contributions of hard and soft sweeps and a surprisingly large proportion of frequency-dependent selection in D. melanogaster populations.

• Korean Journal of Plant Tissue Culture
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• v.25 no.1
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• pp.45-50
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• 1998

Hypocotyl explants of flowering cabbage were precultured on MS medium without kanamycin and then cocultured with Agrobacterium tumefaciens LBA4404;;pGA875 harboring insect resistantce proteinase inhibitor II(PI-II) gene in MS liquid medium adjusted pH 5.5 for 72hr. These explants were transferred to MS medium containing 20 mg/L kanamycin, 500 mg/L carbenicillin, and 1 mg/L BA. The explants were subsequently subcultured every 2 weeks. After 4 weeks of subculture, kanamycin-resistant shoots were obtained from selection medium. Leaves of putative transformants survived on MS selection medium containing 30 mg/L kanamycin. Incoporation of the PI-II gene into flowering cabbage was confirmed by PCR analysis of genomic DNA. Southern blot analysis showed that ECL-labeled probe for PI-II gene was hybridized to the expected amplified genomic DNA fragment of about 500 by from transgenic flowering cabbage.

• Animal Bioscience
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• v.37 no.5
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• pp.807-816
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• 2024

Objective: This study aims to identify the significant regions and candidate genes of growth-related traits (adjusted backfat thickness [ABF], average daily gain [ADG], and days to 90 kg [DAYS90]) in Korean commercial GGP pig (Duroc, Landrace, and Yorkshire) populations. Methods: A genome-wide association study (GWAS) was performed using single-nucleotide polymorphism (SNP) markers for imputation to Illumina PorcineSNP60. The BayesB method was applied to calculate thresholds for the significance of SNP markers. The identified windows were considered significant if they explained ≥1% genetic variance. Results: A total of 28 window regions were related to genetic growth effects. Bayesian GWAS revealed 28 significant genetic regions including 52 informative SNPs associated with growth traits (ABF, ADG, DAYS90) in Duroc, Landrace, and Yorkshire pigs, with genetic variance ranging from 1.00% to 5.46%. Additionally, 14 candidate genes with previous functional validation were identified for these traits. Conclusion: The identified SNPs within these regions hold potential value for future marker-assisted or genomic selection in pig breeding programs. Consequently, they contribute to an improved understanding of genetic architecture and our ability to genetically enhance pigs. SNPs within the identified regions could prove valuable for future marker-assisted or genomic selection in pig breeding programs.

• Journal of Life Science
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• v.32 no.4
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• pp.279-284
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• 2022

This study was conducted to establish a genetic evaluation system applicable to general farms for improving cows raised on farms. The analysis used Best Linear Unbiased Prediction (BLUP) and Genomic Best Linear Unbiased Prediction (GBLUP) for 619 cows raised in Gyeonggi-do Province and compared and analyzed the accuracy of the estimated breeding value according to four traits (carcass weight, loineye muscle area, back fat thickness, and marbling). In the case of the GBLUP method, the size of the reference population was divided into different four groups and analyzed. The analysis results confirmed that the accuracy of the breeding value of each trait increased as the size of the GBLUP reference population increased. Comparing the accuracy of the breeding values estimated using the BLUP and GBLUP methods, it was confirmed that when the breeding values were estimated using the GBLUP method, they increased by 0.10, 0.09, 0.09, and 0.11 for carcass weight, eye muscle area, back fat thickness, and marbling scores, respectively. Applying the GBLUP method to the evaluation and selection of cows can enable precise and accurate individual selection, while increasing the size of the reference population can make even more accurate individual selection possible, thus increasing selection efficiency.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1117-1128
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• 2016

In genetic association studies with high-dimensional genomic data, regularization procedures based on penalized likelihood are often applied to identify genes or genetic regions associated with diseases or traits. A network-based regularization procedure can utilize biological network information (such as genetic pathways and signaling pathways in genetic association studies) with an outstanding selection performance over other regularization procedures such as lasso and elastic-net. However, network-based regularization has a limitation because cannot be applied to high-dimension genomic data with a group structure. In this article, we propose to combine data dimension reduction techniques such as principal component analysis and a partial least square into network-based regularization for the analysis of high-dimensional genomic data with a group structure. The selection performance of the proposed method was evaluated by extensive simulation studies. The proposed method was also applied to real DNA methylation data generated from Illumina Innium HumanMethylation27K BeadChip, where methylation beta values of around 20,000 CpG sites over 12,770 genes were compared between 123 ovarian cancer patients and 152 healthy controls. This analysis was also able to indicate a few cancer-related genes.

• IMMUNE NETWORK
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• v.2 no.4
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• pp.233-241
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• 2002

Background: Monoclonal antibodies (mAb) of rodent origin are produced with ease by hybridoma fusion technique, and have been successfully used as therapeutic reagents for humans after humanization by genetic engineering. However, utilization of these antibodies for therapeutic purpose has been limited by the fact that they act as immunogens in human body causing undesired side effects. So far, there have been several attempts to produce human mAbs for effective in vivo diagnostic or therapeutic reagents including the use of humanized xenomouse that is generated by mating knockout mice which lost Ig heavy and light chain genes by homologous recombination and transgenic mice having both human Ig heavy and light gene loci in their genome. Methods: Genomic DNA fragments of mouse Ig heavy and light chain were obtained from a mouse brain ${\lambda}$ genomic library by PCR screening and cloned into a targeting vector with ultimate goal of generating Ig knockout mouse. Results: Through PCR screening of the genomic library, three heavy chain and three light chain Ig gene fragments were identified, and restriction map of one of the heavy chain gene fragments was determined. Then heavy chain Ig gene fragments were subcloned into a targeting vector. The resulting construct was introduced into embryonic stem cells. Antibiotic selection of transfected cells is under the progress. Conclusion: Generation of xenomouse is particularly important in medical biotechnology. However, this goal is not easily achieved due to the technical difficulties as well as huge financial expenses. Although we are in the early stage of a long-term project, our results, at least, partially contribute the successful generation of humanized xenomouse in Korea.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.535-546
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• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.