• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.265-271
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• 2008

It is commonly believed that diseases of human or economic traits of livestock are caused not by single genes acting alone, but multiple genes interacting with one another. This issue is difficult due to the limitations of parametric-statistic methods of gene effects. So we introduce multifactor-dimensionality reduction(MDR) as a methods for reducing the dimensionality of multilocus information. The MDR method is nonparametric (i. e., no hypothesis about the value of a statistical parameter is made), model free (i. e., it assumes no particular inheritance model) and is directly applicable to case-control studies. Application of the MDR method revealed the best model with an interaction effect between the SNPs, SNP1 and SNP3, while only one main effect of SNP1 was statistically significant for LMA (p < 0.01) under a general linear mixed model.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.435-442
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• 2009

Multiple genes interacting is a difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction(MDR) statistical method by dummy variables was applied to identify interaction effects of single nucleotide polymorphisms(SNPs) responsible for longissimus mulcle dorsi area(LMA), carcass cold weight(CWT) and average daily gain(ADG) in a Hanwoo beef 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.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.6
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• pp.784-788
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• 2008

Studies to detect genes responsible for economic traits in farm animals have been performed using parametric linear models. A non-parametric, model-free approach using the 'expanded multifactor-dimensionality reduction (MDR) method' considering high dimensionalities of interaction effects between multiple single nucleotide polymorphisms (SNPs), was applied to identify interaction effects of SNPs responsible for carcass traits in a Hanwoo beef cattle population. Data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, and comprised 299 steers from 16 paternal half-sib proven sires that were delivered in Namwon or Daegwanryong livestock testing stations between spring of 2002 and fall of 2003. For each steer at approximately 722 days of age, the Longssimus dorsi muscle area (LMA) was measured after slaughter. Three functional SNPs (19_1, 18_4, 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the QTL for meat quality were previously detected, were assessed. Application of the expanded MDR method revealed the best model with an interaction effect between the SNPs 19_1 and 28_2, while only one main effect of SNP19_1 was statistically significant for LMA (p<0.01) under a general linear mixed model. Our results suggest that the expanded MDR method better identifies interaction effects between multiple genes that are related to polygenic traits, and that the method is an alternative to the current model choices to find associations of multiple functional SNPs and/or their interaction effects with economic traits in livestock populations.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.9
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• pp.1218-1228
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• 2013

Fat quality is determined by the composition of fatty acids. Genetic relationships between this composition and single nucleotide polymorphisms (SNPs) in the stearoyl-CoA desaturase1 (SCD1) gene were examined using 513 Korean native cattle. Single and epistatic effects of 7 SNP genetic variations were investigated, and the multifactor dimensionality reduction (MDR) method was used to investigate gene interactions in terms of oleic acid (C18:1), mono-unsaturated fatty acids (MUFAs) and marbling score (MS). The g.6850+77 A>G and g.14047 C>T SNP interactions were identified as the statistically optimal combination (C18:1, MUFAs and MS permutation p-values were 0.000, 0.000 and 0.001 respectively) of two-way gene interactions. The interaction effects of g.6850+77 A>G, g.10213 T>C and g.14047 C>T reflected the highest training-balanced accuracy (63.76%, 64.70% and 61.85% respectively) and was better than the individual effects for C18:1, MUFAs and MS. In addition, the superior genotype groups were AATTCC, AGTTCC, GGTCCC, AGTCCT, GGCCCT and AGCCTT. These results suggest that the selected SNP combination of the SCD1 gene and superior genotype groups can provide useful inferences for the improvement of the fatty acid composition in Korean native cattle.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.67-74
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• 2009

In order to make the high quality Korean cattle, it has been identified the gene markers which influence to various economic traits. To identify statistically significances among SNP markers, Lee et. al. (2008b) identified SNP(19_1)$^*$SNP(28_2) marker was an important marker in LMA(longissimus muscle dorsi area). In addition, CWT(carcass cold weight) and ADG(average daily gain) are applied for expanded multifactor dimensionality reduction (expanded MDR) method from the comprehensive economic traits. The results showed that SNP(19_1)$^*$SNP(28_2) interaction marker was good and a very meaningful for economic traits.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1271-1280
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• 2010

We have used multifactor dimensionality reduction (MDR) method to study genegene interaction effect of statistical model in general. But, MDR method could not be applied in the continuous data. In this paper, continuous-type data by the support vector machine (SVM) algorithm are proposed to the MDR method which provides an introduction to the technique. Also we apply the method on the identify major interaction effects of single nucleotide polymorphisms (SNPs) responsible for economic traits in a Korean cattle population.

• Genomics & Informatics
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• v.18 no.4
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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.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.867-876
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• 2013

Metabolic syndrome has been known as a major factor of cardiovascular disease. Several metabolic disorders, particularly chronic disease is complex, and from individuals that appear in our country, the prevalence of the metabolic syndrome is increasing gradually. Therefore, this study, using a multi-factor dimensionality reduction method, checks the major single risk factor of metabolic syndrome and suggests a new diagnosis results of metabolic syndrome. Data of 3990 adults who responded to all the questionnaires of health interview are used from the database of the 5th Korea national health and nutrition examination survey conducted in 2010. As the result, the most dangerous single risk factor for metabolic syndrome was waist circumference and the most dangerous combination factors were waist circumference, triglyceride, and hypertension. This is the result of a new diagnosis of the metabolic syndrome. Especially, waist circumference, low HDL-cholesterol and hypertension were the most dangerous combination for male. In particular, the combination of waist circumference, triglyceride and diabetes was dangerous for obese people.