• Journal of Microbiology and Biotechnology
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• v.24 no.4
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• pp.563-567
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• 2014

Pseudomonas syringae pv. syringae (Psy) is a major bacterial pathogen of many economically important plant species. Despite the severity of its impact, the genome sequence of the type strain has not been reported. Here, we present the draft genome sequence of Psy ATCC 19310. Comparative genomic analysis revealed that Psy ATCC 19310 is closely related to Psy B728a. However, only a few type III effectors, which are key virulence factors, are shared by the two strains, indicating the possibility of host-pathogen specificity and genome dynamics, even under the pathovar level.

• The Plant Pathology Journal
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• v.38 no.2
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• pp.167-174
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• 2022

Pseudomonas amygdali is a hemibiotrophic phytopathogen that causes disease in woody and herbaceous plants. Complete genomes of four P. amygdali pathovars were comparatively analyzed to decipher the impact of genomic diversity on host colonization. The pan-genome indicated that 3,928 core genes are conserved among pathovars, while 504-1,009 are unique to specific pathovars. The unique genome contained many mobile elements and exhibited a functional distribution different from the core genome. Genes involved in O-antigen biosynthesis and antimicrobial peptide resistance were significantly enriched for adaptation to hostile environments. While the type III secretion system was distributed in the core genome, unique genomes revealed a different organization of secretion systems as follows: type I in pv. tabaci, type II in pv. japonicus, type IV in pv. morsprunorum, and type VI in pv. lachrymans. These findings provide genetic insight into the dynamic interactions of the bacteria with plant hosts.

• Genomics & Informatics
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• v.17 no.4
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• pp.48.1-48.6
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• 2019

Over the last decade, genome-wide association studies (GWASs) have provided an unprecedented amount of genetic variations that are associated with various phenotypes. However, previous GWAS were mostly conducted in European populations, and these biased results for non-Europeans may result in a significant reduction in risk prediction for non-Europeans. An issue with the early GWAS was the winner's curse problem, which led to misleading results when constructing the polygenic risk scores (PRS). Therefore, more non-European population-based studies are needed to validate reported variants and improve genetic risk assessment across diverse populations. In this study, we validated 422 variants independently associated with glycemic indexes, liver enzymes, and type 2 diabetes in 125,872 samples from a Korean population, and further validated the results by assessing publicly available summary statistics from European GWAS (n = 898,130). Among the 422 independently associated variants, 284, 320, and 361 variants were replicated in Koreans, Europeans, and either one of the two populations. In addition, the effect sizes for Koreans and Europeans were moderately correlated (r = 0.33-0.68). However, 61 variants were not replicated in both Koreans and Europeans. Our findings provide valuable information on effect sizes and statistical significance, which is essential to improve the assessment of disease risk using PRS analysis.

• Microbiology and Biotechnology Letters
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• v.51 no.4
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• pp.566-568
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• 2023

In this report, we present the whole-genome sequence of Bifidobacterium bifidum DS0908 isolated from the human fecal sample. The genome composed of a single circular chromosome is 2,223,317 bp long and the DNA G+C content is 62.65%. No virulence genes were detected in the genomic sequences of B. bifidum DS0908.

• Journal of Microbiology and Biotechnology
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• v.19 no.2
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• pp.140-146
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• 2009

MAPSI(Management and Analysis for Polyketide Synthase Type I) has been developed to offer computational analysis methods to detect type I PKS(polyketide synthase) gene clusters in genome sequences. MAPSI provides a genome analysis component, which detects PKS gene clusters by identifying domains in proteins of a genome. MAPSI also contains databases on polyketides and genome annotation data, as well as analytic components such as new PKS assembly and domain analysis. The polyketide data and analysis component are accessible through Web interfaces and are displayed with diverse information. MAPSI, which was developed to aid researchers studying type I polyketides, provides diverse components to access and analyze polyketide information and should become a very powerful computational tool for polyketide research. The system can be extended through further studies of factors related to the biological activities of polyketides.

• Genomics & Informatics
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• v.14 no.4
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• pp.160-165
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• 2016

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Genomics & Informatics
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• v.19 no.4
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• pp.37.1-37.7
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• 2021

Genome-wide association studies (GWASs) facilitated the discovery of countless disease-associated variants. However, GWASs have mostly been conducted in European ancestry samples. Recent studies have reported that these European-based association results may reduce disease prediction accuracy when applied in non-Europeans. Therefore, previously reported variants should be validated in non-European populations to establish reliable scientific evidence for precision medicine. In this study, we validated known associations with type 2 diabetes (T2D) and related metabolic traits in 125,850 samples from a Korean population genotyped by the Korea Biobank Array (KBA). At the end of December 2020, there were 8,823 variants associated with glycemic traits, lipids, liver enzymes, and T2D in the GWAS catalog. Considering the availability of imputed datasets in the KBA genome data, publicly available East Asian T2D summary statistics, and the linkage disequilibrium among the variants (r² < 0.2), 2,900 independent variants were selected for further analysis. Among these, 1,837 variants (63.3%) were statistically significant (p ≤ 0.05). Most of the non-replicated variants (n = 1,063) showed insufficient statistical power and decreased minor allele frequencies compared with the replicated variants. Moreover, most of known variants showed <10% genetic heritability. These results could provide valuable scientific evidence for future study designs, the current power of GWASs, and future applications in precision medicine in the Korean population.

• The Korea Genome Conference
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• 2006.09a
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• pp.58-58
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• 2006

• Research in Plant Disease
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• v.29 no.3
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• pp.321-329
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• 2023

The bacterium Neobacillus endophyticus BRMEA1^T, isolated from the medicinal plant Selaginella involvens, known as its thermotolerant can grow at 50℃. To explore the genetic basis for its heat tolerance response and its potential for producing valuable natural compounds, the genomes of two thermotolerant and four mesophilic strains in the genus Neobacillus were analyzed using a bioinformatic software platform. The whole genome was annotated using RAST SEED and OrthVenn2, with a focus on identifying potential heattolerance-related genes. N. endophyticus BRMEA1^T was found to possess more stress response genes compared to other mesophilic members of the genus, and it was the only strain that had genes for the synthesis of osmoregulated periplasmic glucans. This study sheds light on the potential value of N. endophyticus BRMEA1^T, as it reveals the mechanism of heat resistance and the application of secondary metabolites produced by this bacterium through whole-genome sequencing and comparative analysis.