• Genomics & Informatics
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• 제14권4호
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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.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1189-1195
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• 2004

This paper investigates construction of gene (interaction) networks from gene expression time-series data based on evolutionary computation. To illustrate the proposed approach in a comprehensive way, we first assume an artificial gene network and then compare it with the reconstructed network from the gene expression time-series data generated by the artificial network. Next, we employ real gene expression time-series data (Spellman's yeast data) to construct a gene network by applying the proposed approach. From these experiments, we find that the proposed approach can be used as a useful tool for discovering the structure of a gene network as well as the corresponding relations among genes. The constructed gene network can further provide biologists with information to generate/test new hypotheses and ultimately to unravel the gene functions.

• Genomics & Informatics
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• 제18권4호
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• pp.38.1-38.9
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• 2020

Chronotype is an important moderator of psychiatric illnesses, which seems to be controlled in some part by genetic factors. Clock genes are the most relevant genes for chronotype. In addition to the roles of individual genes, gene-gene interactions of clock genes substantially contribute to chronotype. We investigated genetic associations and gene-gene interactions of the clock genes BHLHB2, CLOCK, CSNK1E, NR1D1, PER1, PER2, PER3, and TIMELESS for chronotype in 1,293 healthy Korean individuals. Regression analysis was conducted to find associations between single nucleotide polymorphism (SNP) and chronotype. For gene-gene interaction analyses, the quantitative multifactor dimensionality reduction (QMDR) method, a nonparametric model-free method for quantitative phenotypes, were performed. No individual SNP or haplotype showed a significant association with chronotype by both regression analysis and single-locus model of QMDR. QMDR analysis identified NR1D1 rs2314339 and TIMELESS rs4630333 as the best SNP pairs among two-locus interaction models associated with chronotype (cross-validation consistency [CVC] = 8/10, p = 0.041). For the three-locus interaction model, the SNP combination of NR1D1 rs2314339, TIMELESS rs4630333, and PER3 rs228669 showed the best results (CVC = 4/10, p < 0.001). However, because the mean differences between genotype combinations were minor, the clinical roles of clock gene interactions are unlikely to be critical.

• 응용통계연구
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• 제25권4호
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• pp.563-573
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• 2012

Various methods of analysis have been proposed to understand the gene-disease relation and gene-gene interaction effect for a disease through comparison of genotype in case-control study. In this study, we proposed the method to detect a genetic association and gene-gene interaction through the use of a network graph and centrality measures that are used in social network analysis. The applicability of the proposed method was studied through an analysis of real genetic data.

• Genomics & Informatics
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• 제16권4호
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• pp.38.1-38.3
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• 2018

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.

• Genomics & Informatics
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• 제5권3호
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.

• 생명과학회지
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• 제6권3호
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• pp.185-192
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• 1996

박테리오파지 T7 gene 2.5 단백질은 single-stranded DNA 결합 단백질로 박태리오파지 T7의 DNA복제, 재조합, 및 수선에 필수적으로 요구된다. Gene 2.5 protein은 T7의 DNA 합성과 성장에 필수적인 단백질이다. Gene 2.5 Protein이 중요시 되는 이유는 이 단백질이 T7의 다른 복제 필수단백질인 T7의 다른 복제 필수단백질인 T7 DNA polymerase 와 gene 4 protein(helicase/primase)와 서로 상호작용할 것으로 제안되었기 때문이다. (Kim and Richardson, J. Biol. Chem., 1992;1994). 이 단백질의 단백질 상호작용을 가능하게 하는 domain은 carboxyl-terminal domain일 것으로 여러 실험에서 대두되었기에, 이 domain의 특성을 파악하기 위해 야생형과 변이체 gene 2.5 단백질들을 각각 GST에 융합한후 fusion 단백질을 정제하였다. 정제된 이 융합 단백질들의 carboxyl-terminal domain이 T7 복제 단백질들과 상호작용을 조사하는지를 조사하기 위해 affinity chromatography로 이용하였다. 실험 결과, 아생형 GST-gene 2.5 융합단잭질(GST-2.5 (WT))는 T7 DNA polymerase 와 상호작용을 하였지만. 변이형 융합단백질(GST-2.5$\Delta$21C)는 interaction을 하지 못했다. 이 결과는 carbohyl-terminal domain이 단백질-단백질 상호작용을 하는데 직접적으로 관여하는 것을 증명하였다. 또한,GST2.5(WT)는 gene 4 protein(helicase/primase)와 직접 상호작용을 하나. GST2.5$\Delta$21C는 상호작용을 하지 못하는 것으로 나타났다. 따라서 gene 4 proteins와의 상호작용에도 gene 2.5 protein의 carboxyl-terminal domain이 직접 관여 한다는 것이 증명되었다. 이상의 결과에서 gene 2.5 protein은 박테리오파지 T7 의 유전자 목제 시 단백질-단백질 상호작용에 관혀아며, 특히 gene 2.5 protein의 carboxyl-terminal domain이 이러한 상호작용에 직접적으로 관여하는 domain이라는 것을 알 수가 있었다.

• Genomics & Informatics
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• 제16권4호
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• pp.37.1-37.3
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• 2018

Gene-gene interaction is a key factor for explaining missing heritability. Many methods have been proposed to identify gene-gene interactions. Multifactor dimensionality reduction (MDR) is a well-known method for the detection of gene-gene interactions by reduction from genotypes of single-nucleotide polymorphism combinations to a binary variable with a value of high risk or low risk. This method has been widely expanded to own a specific objective. Among those expansions, fuzzy-MDR uses the fuzzy set theory for the membership of high risk or low risk and increases the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow, because it is implemented by R script language. Therefore, in this study, we implemented EFMDR using RCPP ($c^{{+}{+}}$ package) for faster executions. Our implementation for faster EFMDR, called EMMDR-Fast, is about 800 times faster than EFMDR written by R script only.

• Genomics & Informatics
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• 제14권4호
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• Journal of the Korean Data and Information Science Society
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• 제24권2호
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• pp.277-287
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• 2013

광범위 유전자 관련 연구에서는 유전자-유전자 상호작용을 규명하는 것은 매우 중요하다. 최근 유전자-유전자 상호작용을 규명하는데에 대한 많은 연구가 진행되고 있다. 그 중 하나로 더미 다중인자 차원축소법이다. 이 연구의 목적은 모의실험을 통해 유전자-유전자 상호작용 파악하기 위한 더미 다중인자 차원축소의 검증력을 평가하는 것이다. 또한 이 방법을 적용하여 한우모집단에서 경제형질을 위한 단일 염기 다형성의 상호작용 효과를 확인하였다.