• 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.

• 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.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• 제13권5호
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• pp.2031-2037
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• 2012

Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial human disease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes or environmental exposures. Based on our previous case-control study in Chongqing of China, we have found increased risk of colorectal cancer exists in individuals carrying a novel homozygous TT at locus rs1329149 and known homozygous AA at locus rs671. Methods: In this study, we proposed statistical method-crossover analysis in combination with logistic regression model, to further analyze our data and focus on assessing gene-environmental interactions for colorectal cancer. Results: The results of the crossover analysis showed that there are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactorial logistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicative interaction with alcohol consumption. Moreover, we also found additive interactions between any pair of two factors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through the crossover analysis, which was not evident on logistic regression. Conclusions: In conclusion, the method based on crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environment interactions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption in the pathogenesis and development of colorectal cancer.

• 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.

• Journal of Preventive Medicine and Public Health
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• 제42권6호
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• pp.360-370
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• 2009

Genetic factors clearly play a role in carcinogenesis, but migrant studies provide unequivocal evidence that environmental factors are critical in defining cancer risk. Therefore, one may expect that the lower availability of substrate for biochemical reactions leads to more genetic changes in enzyme function; for example, most studies have indicated the variant MTHFR genotype 677TT is related to biomarkers, such as homocysteine concentrations or global DNA methylation particularly in a low folate diet. The modification of a phenotype related to a genotype, particularly by dietary habits, could support the notion that some of inconsistencies in findings from molecular epidemiologic studies could be due to differences in the populations studied and unaccounted underlying characteristics mediating the relationship between genetic polymorphisms and the actual phenotypes. Given the evidence that diet can modify cancer risk, gene-diet interactions in cancer etiology would be anticipated. However, much of the evidence in this area comes from observational epidemiology, which limits the causal inference. Thus, the investigation of these interactions is essential to gain a full understanding of the impact of genetic variation on health outcomes. This report reviews current approaches to gene-diet interactions in epidemiological studies. Characteristics of gene and dietary factors are divided into four categories: one carbon metabolism-related gene polymorphisms and dietary factors including folate, vitamin B group and methionines; oxidative stress-related gene polymorphisms and antioxidant nutrients including vegetable and fruit intake; carcinogen-metabolizing gene polymorphisms and meat intake including heterocyclic amins and polycyclic aromatic hydrocarbon; and other gene-diet interactive effect on cancer.

• 응용통계연구
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• 제24권5호
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• pp.861-869
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• 2011

최근 유전학에서 주요 목표중 하나는 복합질환에 영향을 미치는 유전적 요인을 찾아내는 것이다. 유전자좌간의 상호작용이 있을 때에는 단일 유전자좌 분석으로는 이러한 목표를 달성하기 어려우므로, 유전자-유전자간 상호작용이나 유전자-환경인자간 상호작용분석을 고려할 필요가 있다. 자주 사용되는 MDR(multifactor dimensionality reduction)방법은 데이터를 고위험군과 저위험군으로 각각 병합하여 사용하므로 특정 유전자형에서 차이가 나는 경우에는 이를 찾아내기 어렵다. 본 연구에서는 이러한 점을 보완하도록 유전자형 조합에서의 대조군과 질환군의 상대위험도를 이용하여 유전자-유전자간 상호작용을 탐색하는 방법을 제안하였다. MDR 공개데이터와 8가지 유전모형으로부터 생성한 모의자료의 분석을 통해 방법의 유용성을 확인하였다.

• Genomics & Informatics
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• 제10권4호
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• pp.256-262
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• 2012

Most common complex traits, such as obesity, hypertension, diabetes, and cancers, are known to be associated with multiple genes, environmental factors, and their epistasis. Recently, the development of advanced genotyping technologies has allowed us to perform genome-wide association studies (GWASs). For detecting the effects of multiple genes on complex traits, many approaches have been proposed for GWASs. Multifactor dimensionality reduction (MDR) is one of the powerful and efficient methods for detecting high-order gene-gene ($G{\times}G$) interactions. However, the biological interpretation of $G{\times}G$ interactions identified by MDR analysis is not easy. In order to aid the interpretation of MDR results, we propose a network graph analysis to elucidate the meaning of identified $G{\times}G$ interactions. The proposed network graph analysis consists of three steps. The first step is for performing $G{\times}G$ interaction analysis using MDR analysis. The second step is to draw the network graph using the MDR result. The third step is to provide biological evidence of the identified $G{\times}G$ interaction using external biological databases. The proposed method was applied to Korean Association Resource (KARE) data, containing 8838 individuals with 327,632 single-nucleotide polymorphisms, in order to perform $G{\times}G$ interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified $G{\times}G$ interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of $G{\times}G$ interactions.

• 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.

• 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.