• Molecular & Cellular Toxicology
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• v.2 no.2
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• pp.75-80
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• 2006

Biomarkers identify various stages and interactions on the pathway from exposure to disease. The three categories of biomarkers are those measuring susceptibility, exposure and effect. Susceptibility biomarkers are identifiable genetic variations affecting absorption, metabolism or response to environmental agents. Biomarkers of exposure indicate the amount of a foreign compound that is absorbed into the body. Biological measurements performed on human tissues are vastly expanding the capabilities of classical epidemiology, which has relied primarily on estimates of human exposure derived form chemical levels in the air, water, and other exposure routes. Biomarkers of exposure indicate the amount of a foreign compound that is absorbed into the body. Biological measurements performed on human tissues are vastly expanding the capabilities of classical epidemiology, which has relied primarily on estimates of human exposure derived form chemical levels in the air, water, and other exposure routes. The biomarker response is typical of chemical pollution by specific classes of compound, such as (i) heavy metals (mercury, cadmium, lead, zinc), responsible for the induction of metallothionein synthesis, and (ii) organochlorinated pollutants (PCBs, dioxins, DDT congeners) and polycyclic aromatic hydrocarbons (PAHs), which induce the mixed function oxygenase (MFO) involved in their bio transformations and elimination. Currently genomic researches are developed in human cDNA clone subarrays oriented toward the expression of genes involved in responses to xenobiotic metabolizing enzymes, cell cycle components, oncogenes, tumor suppressor genes, DNA repair genes, estrogen-responsive genes, oxidative stress genes, and genes known to be involved in apoptotic cell death. Several research laboratories in Korea for kicking off these Environmental Genomics were summarized.

• Genomics & Informatics
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• v.5 no.4
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• pp.168-173
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• 2007

In previous nuclear genomic association studies, Random Forests (RF), one of several up-to-date machine learning methods, has been used successfully to generate evidence of association of genetic polymorphisms with diseases or other phenotypes. Compared with traditional statistical analytic methods, such as chi-square tests or logistic regression models, the RF method has advantages in handling large numbers of predictor variables and examining gene-gene interactions without a specific model. Here, we applied the RF method to find the association between mitochondrial single nucleotide polymorphisms (mtSNPs) and diabetes risk. The results from a chi-square test validated the usage of RF for association studies using mtDNA. Indexes of important variables such as the Gini index and mean decrease in accuracy index performed well compared with chi-square tests in favor of finding mtSNPs associated with a real disease example, type 2 diabetes.

• Journal of environmental and Sanitary engineering
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• v.14 no.4
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• pp.99-109
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• 1999

Enteropathogenic Yersina enterocolitica is an important cause of human and animal disease. Phenotypic and genotypic characteristics currently used to identify Yersinia enterocolitica are not necessarily sufficient to differentiate pathogenic from non-pathogenic strains or to analyze the epidemiology of yersiniae at a molecular level. To improve the characterization of Yersinia enterocolitica, A total of 65 isolates of Yersinia enterocolitica were examined with bioserotyping, antibiotic susceptibilities, PFGE, PCR-ribotyping. Genomic DNA pattern generated by PFGE are highly specific for different strains of an organism and have significant value in epidemiologic investigations. The PFGE analysis of Not I-digested chromosomal DNA of Y. enterocolitica were performed with a CHEF Mapper(Bio-Rad, USA). Not I generated 19 restriction endonuclease digestion profiles(REDP). PCR-ribotyping, performed with primers complementry to conserved regions of 16S and 23S rRNA gene, generated 13 ribotypes. PCR-ribotyping can be considered a good technich for subtyping strains of Y.enterocolitica.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.815-825
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• 2016

Marginalized random effects models (MREMs) are often used to analyze longitudinal categorical data. The models permit direct estimation of marginal mean parameters and specify the serial correlation of longitudinal categorical data via the random effects. However, it is not easy to estimate the random effects covariance matrix in the MREMs because the matrix is high-dimensional and must be positive-definite. To solve these restrictions, we introduce two modeling approaches of the random effects covariance matrix: partial autocorrelation and the modified Cholesky decomposition. These proposed methods are illustrated with the real data from Korean genomic epidemiology study.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.235-240
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• 2013

Generalized linear mixed models(GLMMs) are frequently used for the analysis of longitudinal categorical data when the subject-specific effects is of interest. In GLMMs, the structure of the random effects covariance matrix is important for the estimation of fixed effects and to explain subject and time variations. The estimation of the matrix is not simple because of the high dimension and the positive definiteness; subsequently, we practically use the simple structure of the covariance matrix such as AR(1). However, this strong assumption can result in biased estimates of the fixed effects. In this paper, we introduce Bayesian modeling approaches for the random effects covariance matrix using a modified Cholesky decomposition. The modified Cholesky decomposition approach has been used to explain a heterogenous random effects covariance matrix and the subsequent estimated covariance matrix will be positive definite. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using these methods.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.522-526
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• 2007

Since newly emerged disease, the Severe Acute Respiratory Syndrome (SARS), spread from Asia to North America and Europe rapidly in 2003, many researchers have tried to determine where the virus came from. In the phylogenetic point of view, SARS virus has been known to be one of the genus Coronavirus, but, the overall conservation of SARS virus sequence was not highly similar to that of known coronaviruses. The natural reservoirs of SARS-CoV are not clearly determined, yet. In the present study, the genomic sequences of SARS-CoV were analyzed by bioinformatics techniques such as multiple sequence alignment and phylogenetic analysis methods as well multivariate statistical analysis. All the calculating processes, including calculations of the relative synonymous codon usage (RSCU) and other genomic parameters using 30,305 coding sequences from the two genera, Coronavirus, and Lentivirus, and one family, Orthomyxoviridae, were performed on SMP cluster in KISTI, Supercomputing Center. As a result, SARS_CoV showed very similar RSCU patterns with feline coronavirus on the both axes of the correspondence analysis, and this result showed more agreeable results with serological results for SARS_CoV than that of phylogenetic result itself. In addition, SARS_CoV, human immunodeficiency virus, and influenza A virus commonly showed the very low RSCU differences among each synonymous codon group, and this low RSCU bias might provide some advantages for them to be transmitted from other species into human beings more successfully. Large-scale genomic analysis using bioinformatics techniques may be useful in genetic epidemiology field effectively.

• Journal of Microbiology and Biotechnology
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• v.15 no.3
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• pp.502-509
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• 2005

Sexual reproduction plays an important role in the biology and epidemiology of oomycete plant pathogens such as the heterothallic species Phytophthora infestans. Recent worldwide dispersal of A2 mating type strains of P. infestans resulted in increased virulence, gene transfer, and genetic variation, creating new challenges for disease management. To develop a genetic assay for mating type identification in P. infestans, we used the Amplified Fragment Length Polymorphism (AFLP) technique. The primer combination E+AT/M+CTA detected a fragment specific to A1 mating type (Mat-A1) of P. infestans. This fragment was cloned and sequenced, and a pair of primers (INF-1, INF-2) were designed and used to differentiate P. infestans Mat-A1 from Mat-A2 strains. The Mat A1-specific fragment was detected using Southern blot analysis of PCR products amplified with primers INF-1 and INF-2 from genomic DNA of 14 P. infestans Mat-A1 strains, but not 13 P. infestans Mat-A2 strains or 8 other isolates representing several Phytophthora spp. Southern blot analysis of genomic DNAs of P. infestans isolates revealed a 1.6 kb restriction enzyme (EcoRI, BamHI, AvaI)-fragment only in Mat-A1 strains. The A1 mating type-specific primers amplified a unique band under stringent annealing temperatures of $63^{\circ}C-64^{\circ}C$, suggesting that this PCR assay could be developed into a useful method for mating type determination of P. infestans in field material.

• Genomics & Informatics
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• v.5 no.2
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• pp.61-67
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• 2007

The allele frequencies of markers as well as linkage disequilibrium (LD) can be changed in cases due to the LD between markers and the disease allele, exhibiting spurious associations of markers. To identify the true association, classical statistical tests for dealing with confounders have been applied to draw a conclusion as to whether the association of variants comes from LD with the known disease allele. However, a more direct test considering LD using estimated haplotype frequencies may be more efficient. The null hypothesis is that the different allele frequencies of a variant between cases and controls come solely from the increased disease allele frequency and the LD relationship with the disease allele. The haplotype frequencies of controls are estimated using the expectation maximization (EM) algorithm from the genotype data. The estimated frequencies are applied to calculate the expected haplotype frequencies in cases corresponding to the increase or decrease of the causative or protective alleles. The suggested method was applied to previously published data, and several APOE variants showed association with Alzheimer's disease independent from the APOE ${\varepsilon}4$ variant, rs429358, regardless of LD showing significant simulated p-values. The test results support the possibility that there may be more than one common disease variant in a locus.

• Journal of Microbiology and Biotechnology
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• v.33 no.11
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• pp.1457-1466
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• 2023

Enteric fever is caused by typhoidal Salmonella serovars (Typhi, Paratyphi A, Paratyphi B, and Paratyphi C). Owing to the importance of Salmonella serovars in clinics and public hygiene, reliable diagnostics for typhoidal serovars are crucial. This study aimed to develop a novel diagnostic tool for typhoidal Salmonella serovars and evaluate the use of human blood for clinically diagnosing enteric fever. Five genes were selected to produce specific PCR results against typhoidal Salmonella serovars based on the genes of Salmonella Typhi. Heptaplex PCR, including genetic markers of generic Salmonella, Salmonella enterica subsp. enterica, and typhoidal Salmonella serovars, was developed. Typhoidal Salmonella heptaplex PCR using genomic DNAs from 200 Salmonella strains (112 serovars) provided specifically amplified PCR products for each typhoidal Salmonella serovar. These results suggest that heptaplex PCR can sufficiently discriminate between typhoidal and non-typhoidal Salmonella serovars. Heptaplex PCR was applied to Salmonella-spiked blood cultures directly and provided diagnostic results after 12- or 13.5-h blood culture. Additionally, it demonstrated diagnostic performance with colonies recovered from a 6-h blood culture. This study provides a reliable DNA-based tool for diagnosing typhoidal Salmonella serovars that may be useful in clinical microbiology and epidemiology.