• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.29 no.2
• /
• pp.62-72
• /
• 2018

Objectives: The molecular mechanisms underlying attention-deficit hyperactivity disorder (ADHD) remain unclear. Therefore, this study aimed to identify the genetic susceptibility loci for ADHD in Korean children with ADHD. We performed a case-control and a family-based genome-wide association study (GWAS), as well as genome-wide quantitative trait locus (QTL) analyses, for two symptom traits. Methods: A total of 135 subjects (71 cases and 64 controls), for the case-control analysis, and 54 subjects (27 probands and 27 unaffected siblings), for the family-based analysis, were included. Results: The genome-wide QTL analysis identified four single nucleotide polymorphisms (SNPs) (rs7684645 near APELA, rs12538843 near YAE1D1 and POU6F2, rs11074258 near MCTP2, and rs34396552 near CIDEA) that were significantly associated with the number of inattention symptoms in ADHD. These SNPs showed possible association with ADHD in the family-based GWAS, and with hyperactivity-impulsivity in genome-wide QTL analyses. Moreover, association signals in the family-based QTL analysis for the number of inattention symptoms were clustered near genes IL10, IL19, SCL5A9, and SKINTL. Conclusion: We have identified four QTLs with genome-wide significance and several promising candidates that could potentially be associated with ADHD (CXCR4, UPF1, SETD5, NALCN-AS1, ERC1, SOX2-OT, FGFR2, ANO4, and TBL1XR1). Further replication studies with larger sample sizes are needed.

• Genomics & Informatics
• /
• v.12 no.4
• /
• pp.203-207
• /
• 2014

Hepatitis is a common and serious disease for the Korean population. It is caused by a virus, the A and B types of which are plentiful in Koreans. In this study, we tried to find genetic factors for hepatitis through genome-wide association studies. We took 368 cases and 1,500 controls from Anseong and Ansan cohort data. About 300,000 single-nucleotide polymorphisms and 20 epidemiological variables were analyzed. We did not find any meaningful significant single nucleotide polymorphisms, but we confirmed the influence of major epidemiological variables on hepatitis.

• Horticultural Science & Technology
• /
• v.35 no.2
• /
• pp.149-164
• /
• 2017

Next generation sequencing (NGS) technologies have led to the rapid accumulation of genome sequences through whole-genome sequencing and re-sequencing of crop species. Genomic resources provide the opportunity for a new revolution in plant breeding by facilitating the dissection of complex traits. Among vegetable crops, reference genomes have been sequenced and assembled for several species in the Solanaceae and Cucurbitaceae families, including tomato, pepper, cucumber, watermelon, and melon. These reference genomes have been leveraged for re-sequencing of diverse germplasm collections to explore genome-wide sequence variations, especially single nucleotide polymorphisms (SNPs). The use of genome-wide SNPs and high-throughput genotyping methods has led to the development of new strategies for dissecting complex quantitative traits, such as genome-wide association study (GWAS). In addition, the use of multi-parent populations, including nested association mapping (NAM) and multiparent advanced generation intercross (MAGIC) populations, has helped increase the accuracy of quantitative trait loci (QTL) detection. Consequently, a number of QTL have been discovered for agronomically important traits, such as disease resistance and fruit traits, with high mapping resolution. The molecular markers for these QTL represent a useful resource for enhancing selection efficiency via marker-assisted selection (MAS) in vegetable breeding programs. In this review, we discuss current genomic resources and marker-trait association analysis to facilitate genome-assisted breeding in vegetable species in the Solanaceae and Cucurbitaceae families.

• Genomics & Informatics
• /
• v.20 no.4
• /
• pp.49.1-49.7
• /
• 2022

Many packages for a meta-analysis of genome-wide association studies (GWAS) have been developed to discover genetic variants. Although variations across studies must be considered, there are not many currently-accessible packages that estimate between-study heterogeneity. Thus, we propose a python based application called Beta-Meta which can easily process a meta-analysis by automatically selecting between a fixed effects and a random effects model based on heterogeneity. Beta-Meta implements flexible input data manipulation to allow multiple meta-analyses of different genotype-phenotype associations in a single process. It provides a step-by-step meta-analysis of GWAS for each association in the following order: heterogeneity test, two different calculations of an effect size and a p-value based on heterogeneity, and the Benjamini-Hochberg p-value adjustment. These methods enable users to validate the results of individual studies with greater statistical power and better estimation precision. We elaborate on these and illustrate them with examples from several studies of infertility-related disorders.

• Genomics & Informatics
• /
• v.11 no.3
• /
• pp.135-141
• /
• 2013

Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP) genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO) terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait ($p_{corr}$ < 0.05). Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.

• The Korea Genome Conference
• /
• 2006.09a
• /
• pp.50-50
• /
• 2006

• Asian-Australasian Journal of Animal Sciences
• /
• v.27 no.11
• /
• pp.1532-1539
• /
• 2014

Although growth rate is one of the main economic traits of concern in pig production, there is limited knowledge on its epigenetic regulation, such as DNA methylation. In this study, we conducted methyl-CpG binding domain protein-enriched genome sequencing (MBD-seq) to compare genome-wide DNA methylation profile of small intestine and liver tissue between fast- and slow-growing weaning piglets. The genome-wide methylation pattern between the two different growing groups showed similar proportion of CpG (regions of DNA where a cytosine nucleotide occurs next to a guanine nucleotide in the linear sequence) coverage, genomic regions, and gene regions. Differentially methylated regions and genes were also identified for downstream analysis. In canonical pathway analysis using differentially methylated genes, pathways (triacylglycerol pathway, some cell cycle related pathways, and insulin receptor signaling pathway) expected to be related to growth rate were enriched in the two organ tissues. Differentially methylated genes were also organized in gene networks related to the cellular development, growth, and carbohydrate metabolism. Even though further study is required, the result of this study may contribute to the understanding of epigenetic regulation in pig growth.

• The Journal of the Korea Contents Association
• /
• v.10 no.4
• /
• pp.19-27
• /
• 2010

In recent years, high-throughput genome-wide RNA interference screening is emerging as an essential tool to biologists in understanding complex cellular processes. The manual analysis of the large number of images produced in each study spends much time and the labor. Hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. However, those factors such as the region overlapping, a variety of shapes, and non-uniform local characteristics of cellular images become obstacles to efficient cell segmentation. To avoid the problem, a new watershed-based cell segmentation algorithm using a localized segmentation method and a feature vector is proposed in this paper. Localized approach in segmentation resolves the problems caused by a variety of shapes and non-uniform characteristics. In addition, the poor performance of segmentation in overlapped regions can be improved by taking advantage of a feature vector whose component features complement each other. Simulation results show that the proposed method improves the segmentation performance compared to the method in Cellprofiler.

• Animal cells and systems
• /
• v.13 no.2
• /
• pp.97-103
• /
• 2009

Recently, great advance is achieved in the field of genome-wide functional screening using cell-based assay. Here, we briefly introduce well-established and typical cell-based assays of GPCR and some parameters which should be considered for genome-wide functional screening. Because of characters and importance of GPCR as drug targets, several ways of assay systems were devised. Among them, high-content screening (HCS) that is based on the analysis of image by confocal microscope is becoming favorite choice. The advances in this technology have been driven exclusively by industry for their convenience. Now, it is turn for academy to define more detail signaling networks via HCS using cDNA or siRNA libraries at genome-wide level. By isolating novel signaling mediators using cDNA or siRNA library, and postulating them as new candidates for therapeutic target, more understanding about life science and more increased chances to develop therapeutics against human disease will be achieved.

• Journal of Dairy Science and Biotechnology
• /
• v.38 no.3
• /
• pp.142-145
• /
• 2020

Enterococcus faecalis CAUM157, isolated from raw cow's milk, is a Gram-positive, facultatively anaerobic, and non-spore-forming bacterium capable of inhabiting a wide range of environmental niches. E. faecalis CAUM157 was observed to produce a two-peptide bacteriocin that had a wide range of activity against several pathogens, including Listeria monocytogenes, Staphylococcus aureus, and periodontitis-causing bacteria. The whole genome of E. faecalis CAUM157 was sequenced using the PacBio RS II platform, revealing a genome size of 2,972,812 bp with a G+C ratio of 37.44%, assembled into two contigs. Annotation analysis revealed 2,830 coding sequences, 12 rRNAs, and 61 tRNAs. Further, in silico analysis of the genome identified a single bacteriocin gene cluster.