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.
Genotype ; Methods ; Multifactor Dimensionality Reduction

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.
Classification ; Genome-Wide Association Study ; Genotype ; Methods* ; Multifactor Dimensionality Reduction* ; Phenotype

PURPOSE: Eosinophils function as an effector cell in the development of asthma and allergic disease. Eotaxins are cytokines that promote pulmonary eosinophilia via the receptor CCR3. Single-nucleotide polymorphisms (SNPs) in CCR3 and eotaxin genes are associated with asthma. In this study, genetic interactions among SNPs of several eotaxin genes and CCR3 were assessed and their relationship with blood eosinophilia in asthma was examined. METHODS: A total of 533 asthmatics were enrolled in this study. Asthmatics with eosinophilia (>0.5x109/L) were compared with those without eosinophilia (< or =0.5x109/L). Chi-square tests were used to compare SNP frequencies. Two different models were used to evaluate gene-gene interactions: logistic regression and generalized multifactor dimensionality reduction (GMDR). RESULTS: EOT2+304C>A (29L>I) was significantly associated with 3 of the 4 CCR3 SNPs among asthmatics with eosinophilia (P=0.037-0.009). EOT2+304C>A (29L>I) and the CCR3 SNPs were also significantly associated with blood eosinophilia in an interaction model constructed by logistic regression (P=0.0087). GMDR analysis showed that the combination of EOT2+304C>A (29L>I) and CCR3-174C>T was the best model (accuracy=0.536, P=0.005, CVC 9/10). CONCLUSIONS: The epistatic influence of CCR3 on eotaxin gene variants indicates that these variants may be candidate markers for eosinophilia in asthma.
Asthma* ; Cytokines ; Eosinophilia* ; Eosinophils ; Logistic Models ; Multifactor Dimensionality Reduction ; Polymorphism, Single Nucleotide ; Pulmonary Eosinophilia

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 (GxG) interactions. However, the biological interpretation of GxG 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 GxG interactions. The proposed network graph analysis consists of three steps. The first step is for performing GxG 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 GxG 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 GxG interaction analysis of body mass index (BMI). Our network graph analysis successfully showed that many identified GxG interactions have known biological evidence related to BMI. We expect that our network graph analysis will be helpful to interpret the biological meaning of GxG interactions.
Body Mass Index ; Genome-Wide Association Study ; Hypertension ; Multifactor Dimensionality Reduction ; Obesity ; Resin Cements

Here, we aimed to study the effect of the forkhead box O1-insulin receptor substrate 2 (FOXO1-IRS2) gene interaction and the FOXO1 and IRS2 genes-environment interaction for the risk of type 2 diabetes mellitus (T2DM) in a Chinese Han population. We genotyped 7 polymorphism sites of FOXO1 gene and IRS2 gene in 780 unrelated Chinese Han people (474 cases of T2DM, 306 cases of healthy control). The risk of T2DM in individuals with AA genotype for rs7986407 and CC genotype for rs4581585 in FOXO1 gene was 2.092 and 2.57 times higher than that with GG genotype (odds ratio [OR] = 2.092; 95% confidence interval [CI] = 1.178–3.731; P = 0.011) and TT genotype (OR = 2.571; 95% CI = 1.404–4.695; P = 0.002), respectively. The risk of T2DM in individuals with GG genotype for Gly1057Asp in IRS2 gene was 1.42 times higher than that with AA genotype (OR = 1.422; 95% CI = 1.037–1.949; P = 0.029). The other 4 single nucleotide polymorphisms (SNPs) had no significant association with T2DM (P > 0.05). Multifactor dimensionality reduction (MDR) analysis showed that the interaction between SNPs rs7986407 and rs4325426 in FOXO1 gene and waist was the best model confirmed by interaction analysis, closely associating with T2DM. There was an increased risk for T2DM in the case of non-obesity with genotype combined AA/CC, AA/AC or AG/AA for rs7986407 and rs4325426, and obesity with genotype AA for rs7986407 or AA for rs4325426 (OR = 3.976; 95% CI = 1.156–13.675; P value from sign test [P(sign)] = 0.025; P value from permutation test [P(perm)] = 0.000–0.001). Together, this study indicates an association of FOXO1 and IRS2 gene polymorphisms with T2DM in Chinese Han population, supporting FOXO1-obesity interaction as a key factor for the risk of T2DM.
Asian Continental Ancestry Group* ; Diabetes Mellitus, Type 2* ; Genotype ; Humans ; Multifactor Dimensionality Reduction ; Obesity ; Polymorphism, Single Nucleotide

OBJECTIVE: Oxidative stress may be an important pathogenic mechanism in the obesity and metabolic syndrome. The aims of this study was to assess the possible association between the oxidative stress related Glutathione S-Transferase genes (GST-M1, GST-T1, and GST-P1) variants and the olanzapine-induced weight gain in Korean schizophrenic patients. METHODS: We categorized 78 schizophrenic patients into two groups the more than 7% weight gain from baseline (weight gain > or =7%) and the less weight gain (weight gain <7%) groups according to weight change between before and after long-term olanzapine treatment (440+/-288 days). All participants were genotyped for the GST-M1, GST-T1 and GST-P1 genes. Differences in allele frequencies between cohorts with different body weight changes were evaluated by a chi-square analysis and Fisher's exact test. The multifactor dimensionality reduction (MDR) approach was used to analyze gene-gene interactions. RESULTS: Mean body weight gain was 5.42 kg. There was no difference in the null genotype distribution of GST-M1 and -T1 between subjects with body weight gain > or =7% compared to subjects with body weight gain <7% (p>0.05). No significant difference in GST-P1 genotype and allele frequencies were observed between the groups (p>0.05). MDR analysis did not show a significant interaction between the three GST gene variants and susceptibility to weight gain (p>0.05). CONCLUSION: These findings do not support a relationship between the genetic variants of three GST genes (GST-M1, -T1 and -P1) and weight gain in Korean schizophrenic patients receiving olanzapine treatment.
Benzodiazepines ; Body Weight ; Body Weight Changes ; Cohort Studies ; Gene Frequency ; Genotype ; Glutathione ; Glutathione Transferase ; Humans ; Multifactor Dimensionality Reduction ; Obesity ; Oxidative Stress ; Weight Gain

OBJECTIVE: Communication problems are a prevalent symptom of autism spectrum disorders (ASDs), which have a genetic background. Although several genome-wide studies on ASD have suggested a number of candidate genes, few studies have reported the association or linkage of specific endophenotypes to ASDs. METHODS: Forty-two Korean ASD patients who showed a language delay were enrolled in this study with their parents. We performed a genome-wide scan by using the Affymetrix SNP Array 5.0 platform to identify candidate genes responsible for language delay in ASDs. RESULTS: We detected candidate single-nucleotide polymorphisms (SNPs) in chromosome 11, rs11212733 (p-value=9.76x10(-6)) and rs7125479 (p-value=1.48x10(-4)), as a marker of language delay in ASD using the transmission disequilibrium test and multifactor dimensionality reduction test. CONCLUSION: Although our results suggest that several SNPs are associated with language delay in ASD, rs11212733 we were not able to observe any significant results after correction of multiple comparisons. This may imply that more samples may be required to identify genes associated with language delay in ASD.
Autistic Disorder ; Child ; Autism Spectrum Disorder ; Chromosomes, Human, Pair 11 ; Endophenotypes ; Genome-Wide Association Study ; Humans ; Language Development Disorders ; Multifactor Dimensionality Reduction ; Parents ; Polymorphism, Single Nucleotide

OBJECTIVETo analyze the correlation of Chinese medicine syndrome evolvement and cardiovascular: events in patients with stable coronary heart disease (CHD).

METHODSThis prospective cohort study investigated and: collected the clinical information of patients with stable CHD and observed the syndrome type at the baseline and 6-month at follow-up, as well as the cardiovascular events during the 6-month and 12-month follow-up. The patients were divided into the event group and the non-event group. The interaction and the impact of syndrome evolvement on cardiovascular events were examined through multifactor dimensionality reduction (MDR) analysis and the results were verified by Chi-square test.

RESULTSTotally 1,333 of 1,503 stable CHD patients enrolled met the inclusion criteria: of MDR analysis. Among them, 959 (71.9%) cases were males and 374 (28.1%) cases were females. Thirty seven cases had cardiovascular events during 6 to 12 months after the study began. The results of the MDR analysis and verification using Chi-square test showed that the development of cardiovascular events was positively correlated with interaction between blood stasis and toxic syndrome at the baseline, blood stasis at the baseline and qi deficiency at the 6-month follow-up, toxic syndrome at the baseline and qi deficiency at the 6-month follow-up, toxic syndrome at the base line and blood stasis at the 6-month follow-up, qi deficiency and blood stasis at the 6-month follow-up (P<0.05 for all).

CONCLUSIONSBlood stasis, toxic syndrome and qi deficiency are important factors of stable CHD. There: are positive correlation between cardiovascular events and syndrome evolution from blood stasis to qi deficiency, from toxic syndrome to qi deficiency and from toxic syndrome to blood stasis, indicating the pathogenesis of toxin consuming qi, toxin leading to blood-stasis in stable CHD patients prone to recurrent cardiovascular events.

Aged ; Cardiovascular Diseases ; etiology ; Coronary Angiography ; Coronary Disease ; complications ; physiopathology ; Female ; Humans ; Male ; Medicine, Chinese Traditional ; Middle Aged ; Multifactor Dimensionality Reduction ; Syndrome

PURPOSE: Allergic asthma (AA) and rheumatoid arthritis (RA) are immune tolerance-related diseases, and immune tolerance is known to be influenced by costimulatory molecules. In this study, we sought to identify common genetic susceptibility in AA and RA. METHODS: Two hundred cases of AA, 184 cases of RA, and 182 healthy controls were recruited at the Seoul National University Hospital, Seoul, Korea. Eight single nucleotide polymorphisms (SNPs) in five genes coding costimulatory molecules, namely, -318C>T, +49A>G, and 6230G>A in CTLA4, IVS3+17T>C in CD28, -3479T>G and I179V in CD86, -1C>T in CD40, and -3458A>G in CD40LG were scored, and genetic interactions were evaluated by multifactor dimensionality reduction (MDR) analysis. RESULTS: MDR analysis revealed a significant gene-gene interaction between -3479T>G CD86 and -3458A>G CD40LG for AA. Subjects with the T/T genotype of -3479T>G CD86 and the A/A genotype of -3458A>G CD40LG were found to be significantly more likely to develop AA than those with the T/T genotype of -3479T>G CD86 and A/- genotype of -3458A>G CD40LG (adjusted OR, 6.09; 95% CI, 2.89-12.98; logistic regression analysis controlled by age). Similarly those subjects showed a significant risk of developing RA (adjusted OR, 39.35; 95% CI, 15.01-107.00, logistic regression analysis controlled by age). CONCLUSIONS: Our findings suggest that a genetic interaction between CD86 and CD40LG favors the development of both AA and RA.
Arthritis, Rheumatoid* ; Asthma* ; CD40 Ligand ; Clinical Coding ; Genetic Predisposition to Disease ; Genotype ; Immune Tolerance ; Korea ; Logistic Models ; Methods ; Multifactor Dimensionality Reduction ; Polymorphism, Genetic ; Polymorphism, Single Nucleotide ; Risk Factors* ; Seoul

OBJECTIVETo explore the association between the polymorphisms of oncogenes H-ras and L-myc and colorectal cancer risk, and the interaction of those genes.

METHODSThe genotypes of H-ras and L-myc genes were determined by polymerase chain reaction-based restriction fragment length polymorphism analysis. Stratified analysis and logistic model were used to detect the gene-gene interaction. The gene-gene interaction was validated by multifactor dimensionality reduction (MDR) analysis.

RESULTSThe single SNP model showed that the polymorphisms of H-ras and L-myc genes were not significantly related with colorectal cancer risk (P > 0.05). Stratified analysis revealed that among the L-myc LS + SS genotype carriers, those with H-ras TC + CC genotype showed significantly increased risk of rectal cancer than those with TT genotype (OR = 1.81, P = 0.005). The positive interaction between L-myc and H-ras was detected by logistic regression model. The OR of the interaction effect was 2.74 (P = 0.024). This result was confirmed in the MDR model, with 54.83% testing balanced accuracy and 10/10 cross-validation consistency, and the model was still significant after the 1000 times permutation test (P = 0.001).

CONCLUSIONOur findings suggest that the polymorphism of H-ras and L-myc genes is not related to colorectal cancer risk, but there is a synergy between H-ras and L-myc polymorphisms in the development of rectal cancer.

Aged ; Colonic Neoplasms ; genetics ; Colorectal Neoplasms ; genetics ; Female ; Genes, myc ; Genes, ras ; Genetic Predisposition to Disease ; Genotype ; Humans ; Logistic Models ; Male ; Middle Aged ; Multifactor Dimensionality Reduction ; Polymerase Chain Reaction ; methods ; Polymorphism, Restriction Fragment Length ; Polymorphism, Single Nucleotide ; Rectal Neoplasms ; genetics ; Risk ; Surveys and Questionnaires