• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• pp.1057-1067
• /
• 2014

Many genetic variants have been identified to be associated with complex diseases such as hypertension, diabetes and cancers throughout genome-wide association studies (GWAS). However, there still exist a serious missing heritability problem since the proportion explained by genetic variants from GWAS is very weak less than 10~15%. Gene-gene interaction study may be helpful to explain the missing heritability because most of complex disease mechanisms are involved with more than one single SNP, which include multiple SNPs or gene-gene interactions. This paper focuses on gene-gene interactions with the survival phenotype by extending the multifactor dimensionality reduction (MDR) method to the accelerated failure time (AFT) model. The standardized residual from AFT model is used as a residual score for classifying multiple geno-types into high and low risk groups and algorithm of MDR is implemented. We call this method AFT-MDR and compares the power of AFT-MDR with those of Surv-MDR and Cox-MDR in simulation studies. Also a real data for leukemia Korean patients is analyzed. It was found that the power of AFT-MDR is greater than that of Surv-MDR and is comparable with that of Cox-MDR, but is very sensitive to the censoring fraction.

• Asian-Australasian Journal of Animal Sciences
• /
• v.30 no.2
• /
• pp.160-166
• /
• 2017

Objective: This study identifies single-nucleotide polymorphisms (SNP) or gene combinations that affect the flavor and quality of Korean cattle (Hanwoo) by using the SNP Harvester method. Methods: Four economic traits (oleic acid [C18:1], saturated fatty acids), monounsaturated fatty acids, and marbling score) were adjusted for environmental factors in order to focus solely on genetic effects. The SNP Harvester method was used to investigate gene combinations (two-way gene interactions) associated with these economic traits. Further, a multifactor dimensionality reduction method was used to identify superior genotypes in gene combinations. Results: Table 3 to 4 show the analysis results for differences between superior genotypes and others for selected major gene combinations using the multifactor dimensionality reduction method. Environmental factors were adjusted for in order to evaluate only the genetic effect. Table 5 shows the adjustment effect by comparing the accuracy before and after correction in two-way gene interactions. Conclusion: The g.3977-325 T>C and (g.2988 A>G, g.3977-325 T>C) combinations of fatty acid-binding protein4 were the superior gene, and the superior genotype combinations across all economic traits were the CC genotype at g.3977-325 T>C and the AACC, GACC, GGCC genotypes of (g.2988 A>G, g.3977-325 T>C).

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.861-869
• /
• 2011

One of the main objects of recent genetic studies is to understand genetic factors that induce complex diseases. If there are interactions between loci, it is difficult to find such associations through a single-locus analysis strategy. Thus we need to consider the gene-gene interactions and/or gene-environment interactions. The MDR(multifactor dimensionality reduction) method is being used frequently; however, it is not appropriate to detect interactions caused by a small fraction of the possible genotype pairs. In this study, we propose a relative risk interaction explorer that detects interactions through the calculation of the relative risks between the control and disease groups from each genetic combinations. For illustration, we apply this method to MDR open source data. We also compare the MDR and the proposed method using the simulated data eight genetic models.

• Genomics & Informatics
• /
• v.10 no.4
• /
• pp.256-262
• /
• 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.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.3
• /
• pp.677-685
• /
• 2015

Disease of human and economic traits of livestocks are affected a lot by gene combination effect rather than a single gene effect. In this study, we used SNPHarvester method that supplement existing method in order to investigate the interaction of these genes. The used genes are SREBPs (g.3270+10274 C>T, g.13544 T>C) and FABP4 (g.2634+1018 A>T, g.2988 A>G, g.3690 G>A, g.3710 G>C, g.3977-325 T>C, g.4221 A>G) that are closely related to the fatty acid composition affecting the meatiness of Korean cattle. The economic traits which are used are oleic acid (C18:1), monounsaturated fatty acid (MUFA), marbling score (MS). First, we have utilized the SNPHarvester method in order to find excellent gene combination, and then used the multifactor dimensionality reduction method in order to identify excellent genotype in gene combination.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.2
• /
• pp.277-287
• /
• 2013

It is important to detect the gene-gene interaction in GWAS (genome-wide association study). There have been many studies on detecting gene-gene interaction. The one is D-MDR (dummy multifoactor dimensionality reduction) method. The goal of this study is to evaluate the power of D-MDR for identifying gene-gene interaction by simulation. Also we applied the method on the identify interaction effects of single nucleotide polymorphisms (SNPs) responsible for economic traits in a Korean cattle population (real data).

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.1392-1394
• /
• 2010

단일염기다형성 유전형 자료에 대한 유전자형을 얻어내는 기술(genotyping)이 발달함에 따라 분석해야 하는 SNP의 개수가 수십만 개로 증가하였다. 따라서 기존의 연관성 분석(association study)연구 방법을 그대로 적용시키기는 어렵다. 본 논문에서는 상호정보량(mutual information)과 Multifactor dimensionality reduction을 이용하여 대용량의 SNP 유전형자료를 분석하는 방법을 제안하였고, 이 방법을 toluene diisocyanate-induced asthma에 대해 실험해본 결과 높은 판별력을 보이는 모델을 찾을 수 있었다.

• The Korean Journal of Applied Statistics
• /
• v.22 no.6
• /
• pp.1239-1247
• /
• 2009

Study the gene about economical characteristic of human disease or domestic animal is a matter of grave interest, preserve and elevation of gene of Korea cattle is key subject. Studies have been done on the gene of Korea cattle using EST based SNP map, but it is based on statistical model, therefore there are difference between real position and statistical position. These problems are solved using both EST_based SNP map and Gene on sequence by Lee et al. (2009b). We have used multifactor dimensionality reduction(MDR) method to study interaction effect of statistical model in general. But MDR method cannot be applied in all cases. It can be applied to the only case-control data. So, method is suggested E-MDR method using CART algorithm. Also we identified interaction effects of single nucleotide polymorphisms(SNPs) responsible for average daily gain(ADG) and marbling score(MS) using E-MDR method.

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

• Asian-Australasian Journal of Animal Sciences
• /
• v.30 no.6
• /
• pp.765-772
• /
• 2017

Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.