• Proceedings of the Korean Society of Crop Science Conference
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• 2017.10a
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• pp.319-319
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• 2017

• Genomics & Informatics
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• v.1 no.2
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• pp.108-112
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• 2003

We have constructed a molecular linkage map of pepper (Capsicum spp.) in an interspecific $F_2$ population of 107 plants with 320 RFLP, 136 AFLP, and 46 SSR markers. The resulting linkage map consists of 15 linkage groups covering 1,720 cM with an average map distance of 3.7 cM between framework markers. Most RFLP markers ($80\%$) were pepper-derived clones and these markers were evenly distributed all over the genome. Genes for defense and biosynthesis of carotenoids and capsaicinoids were mapped on this linkage map. By using 30 primer combinations, AFLP markers were generated in the $F_2$ population. For development of SSR markers in Capsicum, microsatellites were isolated from two small-insert genomic libraries and the GenBank database. This combined map provides a starting point for high-resolution QTL analysis, gene isolation, and molecular breeding.

• Genomics & Informatics
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• v.10 no.4
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• pp.234-238
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• 2012

Genetic epidemiology studies have established that the natural variation of gene expression profiles is heritable and has genetic bases. A number of proximal and remote DNA variations, known as expression quantitative trait loci (eQTLs), that are associated with the expression phenotypes have been identified, first in Epstein-Barr virus-transformed lymphoblastoid cell lines and later expanded to other cell and tissue types. Integration of the eQTL information and the network analysis of transcription modules may lead to a better understanding of gene expression regulation. As these network modules have relevance to biological or disease pathways, these findings may be useful in predicting disease susceptibility.

• Korean Journal of Plant Resources
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• v.10 no.2
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• pp.200-210
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• 1997

The understanding on the plant genome is accelerated with the fast advance of molecular biological techniques. The molecular dissecting of the plant genome has made possible the precise genotyping the plants, which can be utilized for molecular breeding program. As well, the molecular cloning of genes interested can facilitate the process of gene transfer between intra-and inter-generic taxa. Moreover, the manipulation of the agronomically important QTL genes, which can be rarely performed by the conventional genetic methods, is also possible by the utilization of molecular markers. In addition to these genetical applications, molecular markers are useful in the areas of plant taxonomy and management of germplasm by fingerprinting analysis. This paper describes the theoretical aspects marker technologies and practical applications of each marker technique.

• Proceedings of the Korean Society of Crop Science Conference
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• 2001.05a
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• pp.222-223
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• 2001

• Journal of Ginseng Research
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• v.33 no.3
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• pp.199-205
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• 2009

Ginseng (Panax ginseng C.A. Meyer) is one of the most important medicinal plants in East Asia. Microsatellite or simple sequence repeat (SSR) markers are used in obtaining genetic analysis and authentication in many plants. The present study examined five microsatellites in conjunction with P. ginseng in Korea. The total observed allelic number was 17 (mean = 3.4), and gene diversities varied from 0.078 to 0.543 with an average of 0.314. Through a combined analysis of five loci in 100 ginseng samples, 44 different combined genotypes were observed. Expected and observed heterozygosites ranged from 0.077 to 0.541 (mean = 0.313) and 0.040 to 0.130 (0.083), respectively. All examined loci exhibited deficiency of heterozygosity and deviation from the Hardy-Weinberg equilibrium. Such results may be explained by the non-random mating and inbreeding that has occurred for several hundred years. These microsatellite markers could be used for the study of molecular genetics and the establishment of DNA marker database, as well as authentication of ginseng species and chromosomal mapping of QTL loci in P. ginseng.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.4
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• pp.473-479
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• 2018

Objective: The study was designed to perform a genome-wide association (GWA) and partitioning of genome using Illumina's PorcineSNP60 Beadchip in order to identify variants and determine the explained heritability for the total number of teats in Yorkshire pig. Methods: After screening with the following criteria: minor allele frequency, $MAF{\leq}0.01$; Hardy-Weinberg equilibrium, $HWE{\leq}0.000001$, a pair-wise genomic relationship matrix was produced using 42,953 single nucleotide polymorphisms (SNPs). A genome-wide mixed linear model-based association analysis (MLMA) was conducted. And for estimating the explained heritability with genome- or chromosome-wide SNPs the genetic relatedness estimation through maximum likelihood approach was used in our study. Results: The MLMA analysis and false discovery rate p-values identified three significant SNPs on two different chromosomes (rs81476910 and rs81405825 on SSC8; rs81332615 on SSC13) for total number of teats. Besides, we estimated that 30% of variance could be explained by all of the common SNPs on the autosomal chromosomes for the trait. The maximum amount of heritability obtained by partitioning the genome were $0.22{\pm}0.05$, $0.16{\pm}0.05$, $0.10{\pm}0.03$ and $0.08{\pm}0.03$ on SSC7, SSC13, SSC1, and SSC8, respectively. Of them, SSC7 explained the amount of estimated heritability along with a SNP (rs80805264) identified by genome-wide association studies at the empirical p value significance level of 2.35E-05 in our study. Interestingly, rs80805264 was found in a nearby quantitative trait loci (QTL) on SSC7 for the teat number trait as identified in a recent study. Moreover, all other significant SNPs were found within and/or close to some QTLs related to ovary weight, total number of born alive and age at puberty in pigs. Conclusion: The SNPs we identified unquestionably represent some of the important QTL regions as well as genes of interest in the genome for various physiological functions responsible for reproduction in pigs.

• Korean Journal of Agricultural Science
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• v.40 no.2
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• pp.155-162
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• 2013

Genome-wide association study was performed on data from 266 Hanwoo steers derived from 66 sire using bovine 10K mapping chip in Hanwoo (Korean Cattle). SNPs were excluded from the analysis if they failed in over 5% of the genotypes, had median GC scores below 0.6, had GC scores under 0.6 in less than 90% of the samples, deviated in heterozygosity more than 3 standard deviations from the other SNPs and were out of Hardy-Weinberg equilibrium for a cutoff p-value of $1^{-15}$. Unmapped and SNPs on sex chromosomes were also excluded. A total of 4,522 SNPs were included in the analysis. To test an association between SNP and QTL, GWAS for five genetic mode (additive, dominant, overdominant, recessive and codominant) was implemented in this study. Three SNPs (rs29018694, ss46526851 and rs29018222) at a threshold p< $1.11{\times}10^{-5}$ were detected on BTA12 and BTA21 for dressing percentages in codominant and recessive genetic mode. The G allele for rs29018694 has 4.9% higher dressing percentage than A allele, while the T allele for ss46526851 has 2.57 % higher dressing percentage than C allele. Therefore, rs29018694 SNP showed a bigger effect than the other two SNPs (ss46526851 and rs29018222) in this study. In conclusion, this study identifies three loci with moderate effects and many loci with infinitesimally small effect across genome in Hanwoo.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.12
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• pp.1809-1815
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• 2019

Objective: Copy number variations (CNVs) are a major source of genetic diversity complementary to single nucleotide polymorphism (SNP) in animals. The aim of the study was to perform a comprehensive genomic analysis of CNVs based on high density whole-genome SNP markers in Chinese Dongxiang spotted pigs. Methods: We used customized Affymetrix Axiom Pig1.4M array plates containing 1.4 million SNPs and the PennCNV algorithm to identify porcine CNVs on autosomes in Chinese Dongxiang spotted pigs. Then, the next generation sequence data was used to confirm the detected CNVs. Next, functional analysis was performed for gene contents in copy number variation regions (CNVRs). In addition, we compared the identified CNVRs with those reported ones and quantitative trait loci (QTL) in the pig QTL database. Results: We identified 871 putative CNVs belonging to 2,221 CNVRs on 17 autosomes. We further discarded CNVRs that were detected only in one individual, leaving us 166 CNVRs in total. The 166 CNVRs ranged from 2.89 kb to 617.53 kb with a mean value of 93.65 kb and a genome coverage of 15.55 Mb, corresponding to 0.58% of the pig genome. A total of 119 (71.69%) of the identified CNVRs were confirmed by next generation sequence data. Moreover, functional annotation showed that these CNVRs are involved in a variety of molecular functions. More than half (56.63%) of the CNVRs (n = 94) have been reported in previous studies, while 72 CNVRs are reported for the first time. In addition, 162 (97.59%) CNVRs were found to overlap with 2,765 previously reported QTLs affecting 378 phenotypic traits. Conclusion: The findings improve the catalog of pig CNVs and provide insights and novel molecular markers for further genetic analyses of Chinese indigenous pigs.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.34-34
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• 2022

Capacity building for bioinformatics could be achieved with the systematic training of research staff and higher degree students in the current best practices for analysis of data from 'omic-type experiments. It is anticipated that the KOICA-University of the Philippines Los Baños - International Rice Research Insitute Agricultural Genomics Research Center activities will focus on the use of next generation sequencing technology for genome sequencing and annotation, genome variant discovery for use in GWAS and QTL mapping, and transcriptome analysis of organisms important to agriculture and food security. Such activities require that researchers have high levels of knowledge and skills in bioinformatics in order to gain insights from the results of the experiments performed. In this talk the bioinformatic tools/solutions and online training materials already available will be presented, as well the upcoming resources under development in support of the project.