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

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.8
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• pp.1127-1133
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• 2018

Objective: Genome wide association study was conducted to identify and validate candidate genes associated with fatty acid composition of pork. Methods: A total of 480 purebreed Duroc pigs were genotyped using IlluminaPorcine60k bead chips while the association test was implemented following genome-wide rapid association using Mixed Model and Regression-Genomic Control (GRAMMAR-GC) approach. Results: A total of 25, 29, and 16 single nucleotide polymorphisms (SNPs) were significantly associated with stearic (18:0), oleic (18:1) and saturated fatty acids (SFA), respectively. Genome wide significant variants were located on the same region of swine chromosome 14 (SSC14) that spanned from 120 to 124 Mb. Top SNP ALGA008191 was located at 5 kb near the stearoyl-CoA desaturase (SCD) gene. This gene is directly involved in desaturation of stearic acid into oleic acid. General relationship of significant SNPs showed high linkage disequilibrium thus genome-wide signals was attributed to SCD gene. However, understanding the role of other genes like elongation of very long chain fatty acids-3 (ELOVL3) located on this chromosomal segment might help in further understanding of metabolism and biosynthesis of fatty acids. Conclusion: Overall, this study provides evidence that validates SCD gene as strong candidate gene associated with fatty acid composition in Duroc pigs. Moreover, this study confirms significant SNPs near ELOVL3 gene.

• Journal of Korean Neurosurgical Society
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• v.66 no.4
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• pp.409-417
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• 2023

Objective : The association between boule (BOLL) and endothelin receptor type A (EDNRA) loci and intracranial aneurysm (IA) formation has been reported via genome-wide association studies. We sought to identify genome-wide interactions involving BOLL and EDNRA loci for IA in a Korean adult cohort. Methods : Genome-wide pairwise interaction analyses of BOLL and EDNRA involving 250 patients with IA and 296 controls were performed using the additive effect model after adjusting for confounding factors. Results : Among 512575 single-nucleotide polymorphisms (SNPs), 23 and 11 common SNPs suggested a genome-wide interaction threshold (p<1.25×10^-8) involving rs700651 (BOLL) and rs6841581 (EDNRA). Rather than singe SNP effect of BOLL or EDNRA on IA development, they showed a synergistic effect on IA formation via multifactorial pair-wise interactions. The rs1105980 of PTCH1 gene showed the most significant interaction with rs700651 (natural log-transformed odds ratio [lnOR], 1.53; p=6.41×10^-11). The rs74585958 of RYK gene interacted strongly with rs6841581 (lnOR, -19.91; p=1.64×10^-9). Although, there was no direct interaction between BOLL and EDNRA variants, two EDNRA-interacting gene variants of TNIK (rs11925024 and rs1231) and FTO (rs9302654), and one BOLL-interacting METTL4 gene variant (rs549315) exhibited marginal interaction with BOLL gene. Conclusion : BOLL or EDNRA may have a synergistic effect on IA formation via multifactorial pair-wise interactions.

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

• The Korea Genome Conference
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• 2006.09a
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• pp.46-46
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• 2006

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

• Genomics & Informatics
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• v.12 no.3
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• pp.121-126
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• 2014

Medication adherence is generally defined as the extent of voluntary cooperation of a patient in taking medicine as prescribed. Adherence to long-term treatment with chronic disease is essential for reducing disease comorbidity and mortality. However, medication non-adherence in chronic disease averages 50%. This study was conducted a genome-wide association study to identify the genetic basis of medication adherence. A total of 235 medication non-adherents and 1,067 medication adherents with hypertension or diabetes were used from the Korean Association Resource project data according to the self-reported treatment status of each chronic disease, respectively. We identified four single nucleotide polymorphisms with suggestive genome-wide association. The most significant single nucleotide polymorphism was rs6978712 (chromosome 7, $p=4.87{\times}10^{-7}$), which is located proximal to the GCC1 gene, which was previously implicated in decision-making capability in drug abusers. Two suggestive single nucleotide polymorphisms were in strong linkage disequilibrium ($r^2$ > 0.8) with rs6978712. Thus, in the aspect of decision-making in adherence behavior, the association between medication adherence and three loci proximal to the GCC1 gene seems worthy of further research. However, to overcome a few limitations in this study, defining the standardized phenotype criteria for self-reported adherence should be performed before replicating association studies.

• 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.11 no.3
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• pp.135-141
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• 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.