• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1331-1339
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• 2013

The lipoprotein lipase (LPL) gene can be considered a functional candidate gene that regulates fatty acid composition. Oh etc (2013) investigated the relationship between unsaturated fatty acids and five novel SNPs, and had confirmed that three polymorphic SNPs (c.322G>A, c.329A>T and c.1591G>A) were associated with fatty acid composition. We have used generalized linear model for adjusted environmental effects and multifactor dimensionality reduction (MDR) method to identify gene-gene interaction effect of statistical model in general. We applied the MDR method on the identify major interaction effects of exonic single nucleotide polymorphisms (SNPs) in the LPL gene for economic traits in Korean cattles population.

• Genomics & Informatics
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• v.10 no.4
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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.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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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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• v.7 no.1
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• pp.20-25
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• 2009

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that is characterized by inflammation of the synovial tissue and deterioration of the joint and bone. A recent study reported a potential gene-environment interaction between HLA-DR and smoking. The present study investigated whether a specific gene was related to the association between smoking and the severity of RA (rheumatoid factor levels > 20 IU/ml). We used the resources of the NARAC family collection of GAW 15 databases, and 1139 subjects with RF>20 IU/ml were included in the current analysis. The linkage panel contained 5858 SNP markers, and 5744 SNPs passed quality control criteria. Linear regression analyses, using PLINK software and generalized estimating equation regression models, were used to test for associations between the SNPs and the severity of RA according to smoking groups. Two major findings were established. First, the severity of RA in smokers was associated with rs703618 (p=$6{\times}10^{-5}$), which lies in the intronic region of the stabilin 2 (STAB2) gene on chromosome 12. Second, there were significant differences in the levels of RF between 'ever smokers' and 'never smokers' according to the rs703618 genotype (G/G, A/G, A/A). We investigated whether a specific gene acts as a mediator between smoking and the severity of RA and found that the STAB2 gene could affect this relationship. Our finding indicates that smoking may mediate RA severity by affecting the expression level of a specific gene.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.467-474
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• 2012

Human diseases and livestock economic traits are not typically the result of variation of a single genetic locus, but are rather the result of interplay between interactions among multiple genes and a variety of environmental exposures. We have used linear regression model for adjusted environmental effects and multifactor dimensionality reduction (MDR) method to identify gene-gene interaction effect of statistical model in general. Of course, we use 5 SNPs (single uncleotide polymorphism) which were studied recently by Oh et al. (2011). We apply the MDR (multifactor demensionality reduction) method on the identify major interaction effects of single nucleotide polymorphisms responsible for economic traits in a Korean cattle population.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.693-703
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• 2010

Diseases of human or economical traits of cattles are occured by interaction of genes. We introduce expanded multifactor dimensionality reduction(E-MDR), dummy multifactor dimensionality reduction(D-MDR) and SNPHarvester which are developed to find interaction of genes. We will select interaction of outstanding gene combinations and select final best genotype groups.

• Proceedings of the KSRS Conference
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• v.2
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• pp.623-626
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• 2006

Group of genes controls the functioning of a cell by complex interactions. These interacting gene groups are called Gene Regulatory Networks (GRNs). Two previous data mining approaches, clustering and classification have been used to analyze gene expression data. While these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rule. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and detect gene expression patterns applying FP-growth algorithm. And then, we construct gene regulatory network from frequent gene patterns using chain rule. Finally, we validated our proposed method by showing that our experimental results are consistent with published results.

• 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.

• ETRI Journal
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• v.38 no.1
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• pp.206-215
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• 2016

The detection of gene-gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the "curse of dimensionality" -that is, it works well for two- or three-way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10-way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre-evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.108-113
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• 2007

When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.