Multifactor Dimensionality Reduction Approach Research Articles

Association of Interactions between Metabolic 'Caretaker' Genes, p53, MDM2, and Tobacco Use with the Risk of Oral Cancer: A Multifactor Dimensionality Reduction Approach.

The present study investigated the association of interactions between gene polymorphisms in metabolic 'caretaker' genes (Phase I: CYP1A1, CYP2E1; Phase II: GSTM1, GSTT1), the cell cycle regulatory gene, p53, along with its negative controller, MDM-2, and the environment variable (tobacco). A nonparametric model, multifactor dimensionality reduction (MDR), was applied to analyse these interactions. This case-control study was carried out on 242 subjects. Genomic DNA was extracted from peripheral blood lymphocytes.11 gene variants with an exposure variable (tobacco use) were analysed using MDR to identify the best locus model for gene-gene and gene-environment interactions. Statistical significance was evaluated using a 1000-fold permutation test using MDR permutation testing software (version 1.0 beta 2). The value of p<0.05 was considered statistically significant. The best three-locus model for gene-gene interaction included two of the p53 gene polymorphisms; rs17878362 (intron 3) and rs1042522 (exon 4) and rs6413432 in the Phase I gene, CYP2E1(DraI). The three-locus model to evaluate the gene-environment interaction included two intronic polymorphisms of the p53 gene, that is, rs17878362 (intron 3) and rs1625895 (intron 6), and rs4646903 in the Phase I gene CYP1A1*2C. The interaction graphs revealed independent main effects of the tobacco and p53 polymorphism, rs1042522 (exon 4), and a significant additive interaction effect between rs17878362 (intron 3) and rs1042522 (exon 4). The nonparametric approach highlighted the potential role of tobacco use and variations in the p53 gene as significant contributors to oral cancer risk. The findings of the present study will help implement preventive strategies in both tobacco use and screening using a molecular pathology approach.

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

BackgroundIn genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework.MethodsTo identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC.ResultsSimulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power.ConclusionWe demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

Cytokine genes multi-locus analysis reveals synergistic influence on genetic susceptibility in Indian SLE – A multifactor-dimensionality reduction approach

Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with unclear etiology. Several loci associated with genetic susceptibility for lupus have been described. However, it lacks reports on cytokine gene-gene interactions among SLE patients from Asian population. Epistasis interaction among single nucleotide polymorphisms (SNPs) of cytokine genes in Indian SLE patients was tested using multifactor-dimensionality reduction (MDR) analysis. A total of fourteen SNPs lacking linkage disequilibrium among different cytokines genes were genotyped in a cohort of 200 SLE patients and 201 healthy individuals as controls of Indian origin. A high degree of synergism among Lymphotoxin-α (LT-α), Interleukin-1β (IL-1β) and Interleukin-10 (IL-10) gene polymorphisms was detected in our SLE patients. Furthermore, by virtue of biological inter-relations among different cytokines, a high strength of interactions was observed among pro-inflammatory (IL-1β, IL-6) and anti-inflammatory (IL-10) cytokine gene SNPs. Also, among studied pro-inflammatory cytokines and chemokines, a strong synergistic effect among Tumor Necrosis Factor-α (TNF-α), LT-α and Monocyte Chemo-attractant Protein-1 (MCP-1) SNPs was occurred. A nature of strong interaction among the candidate cytokine genes may speculate a proactive role in causing genetic susceptibility to the disease in SLE patients with Indian origin.

Interaction of thrombophilic SNPs in patients with unexplained infertility-multifactor dimensionality reduction (MDR) model analysis.

Our aim was to evaluate the frequency and SNP-SNP interactions between factor V Leiden (FVL) G1691A, prothrombin G20210A mutation, and C677T MTHFR and PAI-1 4G/5G gene polymorphisms in female IVF patients with unexplained infertility (UI) by using a multifactor dimensionality reduction (MDR) model analysis. A total of 225 subjects were enrolled in the study. There were 105 females in UI group and 120 healthy controls. Designated SNPs were determined by using allele-specific PCR methods. The difference in thrombophilia prevalence was assessed by a chi-square test and logistic regression analysis. Four-locus SNP interaction model was tested using the MDR approach. A ten-fold cross-validation consistency (CVC) and permutation testing were performed. There was a significant difference of MTHFR C677T polymorphism frequency between the groups. Significantly less UI patients had MTHFR CC genotype (p = 0.005), while the risk allele T was more frequent (OR = 1.83, p = 0.0018). Logistic regression determined a significant association only for MTHFR C677T in our patients (TT genotype OR = 2.99). The MDR analysis confirmed the significance of a single-locus model for MTHFR C677T polymorphism (p = 0.015; OR = 2.93). However, the best, significant predictive model was the two-locus model comprising MTHFR C677T and FVL (CVC = 10/10, testing accuracy = 60.95%, p = 0.013; OR = 3.02). The MTHFR C677T polymorphism was significantly associated with UI, with minor allele T being more frequent. Additionally, there was a significantly increased presence of MTHFR C677T with FVL mutation in these patients. Therefore, MTHFR and its interaction with FVL should be recognized as contributing factors in the pathogenesis of infertility.

Gene-gene interaction: the curse of dimensionality.

Identified genetic variants from genome wide association studies frequently show only modest effects on the disease risk, leading to the "missing heritability" problem. An avenue, to account for a part of this "missingness" is to evaluate gene-gene interactions (epistasis) thereby elucidating their effect on complex diseases. This can potentially help with identifying gene functions, pathways, and drug targets. However, the exhaustive evaluation of all possible genetic interactions among millions of single nucleotide polymorphisms (SNPs) raises several issues, otherwise known as the "curse of dimensionality". The dimensionality involved in the epistatic analysis of such exponentially growing SNPs diminishes the usefulness of traditional, parametric statistical methods. With the immense popularity of multifactor dimensionality reduction (MDR), a non-parametric method, proposed in 2001, that classifies multi-dimensional genotypes into one- dimensional binary approaches, led to the emergence of a fast-growing collection of methods that were based on the MDR approach. Moreover, machine-learning (ML) methods such as random forests and neural networks (NNs), deep-learning (DL) approaches, and hybrid approaches have also been applied profusely, in the recent years, to tackle this dimensionality issue associated with whole genome gene-gene interaction studies. However, exhaustive searching in MDR based approaches or variable selection in ML methods, still pose the risk of missing out on relevant SNPs. Furthermore, interpretability issues are a major hindrance for DL methods. To minimize this loss of information, Python based tools such as PySpark can potentially take advantage of distributed computing resources in the cloud, to bring back smaller subsets of data for further local analysis. Parallel computing can be a powerful resource that stands to fight this "curse". PySpark supports all standard Python libraries and C extensions thus making it convenient to write codes to deliver dramatic improvements in processing speed for extraordinarily large sets of data.

Association and gene-gene interaction analyses for polymorphic variants in CTLA-4 and FOXP3 genes: role in susceptibility to autoimmune thyroid disease.

Polymorphic variants of cytotoxic T-lymphocyte antigen-4 (CTLA-4) and forkhead box protein P3 (FOXP3) genes are implicated in dysregulated immune homeostasis and autoimmune disorders. We analyzed the association between CTLA-4 rs231775 and FOXP3 rs3761548, rs3761549 polymorphisms and predisposition to autoimmune thyroid disease (AITD), inclusive of Hashimoto's thyroiditis (HT) and Graves' disease (GD) in South-Indian population. A total of 355 AITD subjects (comprising 275 HT and 80 GD) and 285 randomly selected age- and sex-matched control subjects were genotyped for the aforementioned polymorphisms by PCR-RFLP method. The rs231775 "G" allele was preponderant in HT and GD subjects when compared with controls and exerted a dominant influence on the susceptibility to HT (p = 0.009) and GD (p = 0.02), respectively. There was no allelic association of rs3761548 and rs3761549 polymorphisms with AITD susceptibility, albeit a significant difference in genotype distribution with respect to rs3761549. Haplotype analysis revealed an increased frequency of rs3761548 "C"-rs3761549 "T" in HT and GD subjects, thereby associating it with disease predisposition (p = 0.03). Epistatic interaction analysis by multifactor dimensionality reduction approach revealed redundancy between CTLA-4 and FOXP3 genes in influencing the susceptibility to AITD. The genetic variation in CTLA-4 gene with reference to rs231775 polymorphism contributes to an increased predisposition to HT and GD. Also, in conjunction with FOXP3 gene variants it seems to influence the susceptibility to HT and GD respectively. The significance of these findings in combination with antithyroid antibody screening could plausibly contribute towards meticulous case-finding for effective treatment of HT and GD.

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low-density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi-Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model-based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.

TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

Single nucleotide polymorphism (SNP) interactions can explain the missing heritability of common complex diseases. Many interaction detection methods have been proposed in genome-wide association studies, and they can be divided into two types: population-based and family-based. Compared with population-based methods, family-based methods are robust vs. population stratification. Several family-based methods have been proposed, among which Multifactor Dimensionality Reduction (MDR)-based methods are popular and powerful. However, current MDR-based methods suffer from heavy computational burden. Furthermore, they do not allow for main effect adjustment. In this work we develop a two-stage model-based MDR approach (TrioMDR) to detect multi-locus interaction in trio families (i.e., two parents and one affected child). TrioMDR combines the MDR framework with logistic regression models to check interactions, so TrioMDR can adjust main effects. In addition, unlike consuming permutation procedures used in traditional MDR-based methods, TrioMDR utilizes a simple semi-parameter P-values correction procedure to control type I error rate, this procedure only uses a few permutations to achieve the significance of a multi-locus model and significantly speeds up TrioMDR. We performed extensive experiments on simulated data to compare the type I error and power of TrioMDR under different scenarios. The results demonstrate that TrioMDR is fast and more powerful in general than some recently proposed methods for interaction detection in trios. The R codes of TrioMDR are available at: https://github.com/TrioMDR/TrioMDR.

A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions.

Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGI effects. However, there are several disadvantages of the existing MDR-based approaches, such as the lack of an efficient way of evaluating the significance of multi-locus models and the high computational burden due to intensive permutation. Furthermore, the MDR method does not distinguish marginal effects from pure interaction effects. We propose a two-step unified model based MDR approach (UM-MDR), in which, the significance of a multi-locus model, even a high-order model, can be easily obtained through a regression framework with a semi-parametric correction procedure for controlling Type I error rates. In comparison to the conventional permutation approach, the proposed semi-parametric correction procedure avoids heavy computation in order to achieve the significance of a multi-locus model. The proposed UM-MDR approach is flexible in the sense that it is able to incorporate different types of traits and evaluate significances of the existing MDR extensions. The simulation studies and the analysis of a real example are provided to demonstrate the utility of the proposed method. UM-MDR can achieve at least the same power as MDR for most scenarios, and it outperforms MDR especially when there are some single nucleotide polymorphisms that only have marginal effects, which masks the detection of causal epistasis for the existing MDR approaches. UM-MDR provides a very good supplement of existing MDR method due to its efficiency in achieving significance for every multi-locus model, its power and its flexibility of handling different types of traits. A R package "umMDR" and other source codes are freely available at http://statgen.snu.ac.kr/software/umMDR/ CONTACT: tspark@stats.snu.ac.kr Supplementary data are available at Bioinformatics online.

Using the Generalized Index of Dissimilarity to Detect Gene-Gene Interactions in Multi-Class Phenotypes.

To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project.

Application of the multifactor dimensionality reduction method in evaluation of the roles of multiple genes/enzymes in multidrug-resistant acquisition in Pseudomonas aeruginosa strains.

Multidrug-resistant Pseudomonas aeruginosa (MDRPA) infections are major threats to healthcare-associated infection control and the intrinsic molecular mechanisms of MDRPA are also unclear. We examined 348 isolates of P. aeruginosa, including 188 MDRPA and 160 non-MDRPA, obtained from five tertiary-care hospitals in Guangzhou, China. Significant correlations were found between gene/enzyme carriage and increased rates of antimicrobial resistance (P < 0·01). gyrA mutation, OprD loss and metallo-β-lactamase (MBL) presence were identified as crucial molecular risk factors for MDRPA acquisition by a combination of univariate logistic regression and a multifactor dimensionality reduction approach. The MDRPA rate was also elevated with the increase in positive numbers of those three determinants (P < 0·001). Thus, gyrA mutation, OprD loss and MBL presence may serve as predictors for early screening of MDRPA infections in clinical settings.

Genetic variations of PIP4K2A confer vulnerability to poor antipsychotic response in severely ill schizophrenia patients.

Literature suggests that disease severity and neurotransmitter signaling pathway genes can accurately identify antipsychotic response in schizophrenia patients. However, putative role of signaling molecules has not been tested in schizophrenia patients based on severity of illness, despite its biological plausibility. In the present study we investigated the possible association of polymorphisms from five candidate genes RGS4, SLC6A3, PIP4K2A, BDNF, PI4KA with response to antipsychotic in variably ill schizophrenia patients. Thus in present study, a total 53 SNPs on the basis of previous reports and functional grounds were examined for their association with antipsychotic response in 423 schizophrenia patients segregated into low and high severity groups. Additionally, haplotype, diplotype, multivariate logistic regression and multifactor-dimensionality reduction (MDR) analyses were performed. Furthermore, observed associations were investigated in atypical monotherapy (n = 355) and risperidone (n = 260) treated subgroups. All associations were estimated as odds ratio (OR) and 95% confidence interval (CI) and test for multiple corrections was applied. Single locus analysis showed significant association of nine variants from SLC6A3, PIP4K2A and BDNF genes with incomplete antipsychotic response in schizophrenia patients with high severity. We identified significant association of six marker diplotype ATTGCT/ATTGCT (rs746203-rs10828317-rs7094131-rs2296624-rs11013052-rs1409396) of PIP4K2A gene in incomplete responders (corrected p-value = 0.001; adjusted-OR = 3.19, 95%-CI = 1.46–6.98) with high severity. These associations were further observed in atypical monotherapy and risperidone sub-groups. MDR approach identified gene-gene interaction among BDNF_rs7103411-BDNF_rs1491851-SLC6A3_rs40184 in severely ill incomplete responders (OR = 7.91, 95%-CI = 4.08–15.36). While RGS4_rs2842026-SLC6A3_rs2975226 interacted synergistically in incomplete responders with low severity (OR = 4.09, 95%-CI = 2.09–8.02). Our findings provide strong evidence that diplotype ATTGCT/ATTGCT of PIP4K2A gene conferred approximately three-times higher incomplete responsiveness towards antipsychotics in severely ill patients. These results are consistent with the known role of phosphatidyl-inositol-signaling elements in antipsychotic action and outcome. Findings have implication for future molecular genetic studies as well as personalized medicine. However more work is warranted to elucidate underlying causal biological pathway.

Antioxidant defense enzyme genes and asthma susceptibility: gender-specific effects and heterogeneity in gene-gene interactions between pathogenetic variants of the disease.

Oxidative stress resulting from an increased amount of reactive oxygen species and an imbalance between oxidants and antioxidants plays an important role in the pathogenesis of asthma. The present study tested the hypothesis that genetic susceptibility to allergic and nonallergic variants of asthma is determined by complex interactions between genes encoding antioxidant defense enzymes (ADE). We carried out a comprehensive analysis of the associations between adult asthma and 46 single nucleotide polymorphisms of 34 ADE genes and 12 other candidate genes of asthma in Russian population using set association analysis and multifactor dimensionality reduction approaches. We found for the first time epistatic interactions between ADE genes underlying asthma susceptibility and the genetic heterogeneity between allergic and nonallergic variants of the disease. We identified GSR (glutathione reductase) and PON2 (paraoxonase 2) as novel candidate genes for asthma susceptibility. We observed gender-specific effects of ADE genes on the risk of asthma. The results of the study demonstrate complexity and diversity of interactions between genes involved in oxidative stress underlying susceptibility to allergic and nonallergic asthma.

Gaba transporter SLC6A11 gene polymorphism associated with tardive dyskinesia

Background: Gamma-aminobutyric acid (GABA) insufficiency has been reported to be related to the tardive dyskinesia (TD) susceptibility. Inada et al. (Pharmacogenet Genomics 2008;18:317–23) identified eight genes belonging to GABA receptor signaling pathway that may be involved in TD susceptibility by genome-wide screening and they replicated associations in an independent sample for polymorphisms in SLC6A11 (GABA transporter 3), GABRG3 (c-3 subunit of GABA-A receptor) and GABRB2 (β-2 subunit of GABA-A receptor). In this study, we tried to replicate their finding in a larger Korean sample and find if any of the genes was associated with the susceptibility to TD. Methods: We selected three polymorphisms in SLC6A11 (rs4684742), GABRG3 (rs2061051) and GABRB2 (rs918528) from the previous study. We carried out a case–control study (105 TD and 175 non-TD schizophrenic patients) to identify the association between the three candidate polymorphisms and susceptibility to TD and their epistatic interactions by using the multifactor dimensionality reduction (MDR) algorithm. Results: Among the three variants, SCL6A11 genotypes distribution showed a significant difference between the TD and non-TD patients (P = 0.049). However, GABRG3 and GABRB2 genotype distributions were not associated with TD (P = 0.268 and P = 0.976, respectively). Further, our analyses provided significant evidence for gene–gene interactions (SCL6A11, GABRG3 and GABRB2) in the development of TD. The odds ratio increased to 2.53 (CI = 1.515–4.217, P = 0.0003) when the genetic susceptibility to TD was analyzed with the three genes considered altogether through MDR approach. Conclusion: These results suggest that GABA receptor signaling pathway was associated with the increased susceptibility to TD in Korean schizophrenic patients.

Detecting epistatic effects associated with cotton traits by a modified MDR approach

Genetic expression of a trait is complicated and it is usually associated with many genes including their interactions (epistasis) and genotype-by-environment interactions. Genetic mapping currently focuses primarily on additive models or marginal genetic effects due to the complexity of epistatic effects. Thus, there exists a need to appropriately identify favorable epistatic effects for important biological traits. Several multifactor dimensionality reduction (MDR) based methods are important resources to identify high-order gene–gene interactions. These methods are mainly focused on human genetic studies. Many traits in plant systems are not only quantitatively inherited but also are often measured in repeated field plots under multiple environments. In this study, we proposed a mixed model based MDR approach, which is suitable for inclusion of various fixed and random effects. This approach was used to analyze a cotton data set that included eight agronomic and fiber traits and 20 DNA markers. The results revealed high order epistatic effects were detected for most of these traits using this modified MDR approach.

The Cumulative Effects of Polymorphisms in the DNA Mismatch Repair Genes and Tobacco Smoking in Oesophageal Cancer Risk

The DNA mismatch repair (MMR) enzymes repair errors in DNA that occur during normal DNA metabolism or are induced by certain cancer-contributing exposures. We assessed the association between 10 single-nucleotide polymorphisms (SNPs) in 5 MMR genes and oesophageal cancer risk in South Africans. Prior to genotyping, SNPs were selected from the HapMap database, based on their significantly different genotypic distributions between European ancestry populations and four HapMap populations of African origin. In the Mixed Ancestry group, the MSH3 rs26279 G/G versus A/A or A/G genotype was positively associated with cancer (OR = 2.71; 95% CI: 1.34–5.50). Similar associations were observed for PMS1 rs5742938 (GG versus AA or AG: OR = 1.73; 95% CI: 1.07–2.79) and MLH3 rs28756991 (AA or GA versus GG: OR = 2.07; 95% IC: 1.04–4.12). In Black individuals, however, no association between MMR polymorhisms and cancer risk was observed in individual SNP analysis. The interactions between MMR genes were evaluated using the model-based multifactor-dimensionality reduction approach, which showed a significant genetic interaction between SNPs in MSH2, MSH3 and PMS1 genes in Black and Mixed Ancestry subjects, respectively. The data also implies that pathogenesis of common polymorphisms in MMR genes is influenced by exposure to tobacco smoke. In conclusion, our findings suggest that common polymorphisms in MMR genes and/or their combined effects might be involved in the aetiology of oesophageal cancer.

Association of polymophisms of renin-angiotensin and hemostasis system genes with ischemic stroke in Russians from central Russia

The allele and genotype frequencies of 14 SNPs of renin-angiotensin (REN, AGT, AGTR1, AGTR2, BKR2, and ADRB2) and hemostasis (FGB, F2, F5, F7, ITGB3, SERPINE1, MTHFR) system genes, as well as of the ACE insertion-deletion polymorphism, were analyzed in patients with ischemic stroke and in healthy controls matched by age, sex, and ethnicity. Genotyping was performed through the amplification of the selected gene sequences and subsequent hybridization of the labeled fragments with SNP-specific DNA probes on the biochip. There were no significant differences in the allele frequencies of individual genes between the groups of stroke patients and healthy donors. The contribution of the renin-angiotensin and hemostasis system genes to the genetic susceptibility to ischemic stroke in Russians from central Russia was also assessed using the MDR (Multifactor Dimensionality Reduction) approach. The genotype combination FGB G/- x ACE I/- x MTHFR C/- x SERPINE1 5G/5G, the frequency of which was significantly higher in patients with stroke than in healthy controls, was associated with an increased risk of ischemic stroke (P = 0.03, OR = 2.4, 95%CI 1.1–5.3).

SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.

Choline Acetyltransferase 2384G&gt;A Polymorphism and the Risk of Alzheimer Disease

The potential association between choline acetyltransferase (CHAT) polymorphism and the risk of Alzheimer disease (AD) has been controversial. We examined the main effect of CHAT polymorphism and its interaction with apolipoprotein E (APOE) polymorphism in the development of AD in a well-powered elderly Korean sample. We analyzed CHAT 2384G>A polymorphism and APOE polymorphism among 736 Korean patients with probable AD and 1386 nondemented Korean controls. We tested the association between AD and CHAT genotype using a logistic regression model. In addition, we used generalized multifactor dimensionality reduction to investigate the interaction between CHAT and APOE with regard to the risk of AD. The CHAT A allele was associated with AD risk in a dose-dependent manner (odds ratio=1.40, 95% confidence interval=1.06-1.85, P=0.018 for heterozygotes; and odds ratio=3.92, 95% confidence interval=1.78-8.58, P=0.001 for homozygotes). The generalized multifactor dimensionality reduction approach identified a significant gene-gene interaction between CHAT and APOE (Balanced accuracy score=0.647, P=0.001). The CHAT A/A genotype was associated with earlier onset of AD (F=5.070, df=2, P=0.007). The CHAT A allele was associated with AD risk in a dose-dependent manner, and its interaction with the APOE ε4 allele was significant with regard to the development of AD. The CHAT A allele was also associated with earlier onset and possibly accelerated progression of AD.

Lack of association of polymorphic variants of genes encoding zinc transporters with the risk of orofacial cleft-affected pregnancies.

Maternal zinc deficiency seems to be a risk factor for orofacial clefts in offspring. This study was undertaken to investigate the involvement of polymorphic variants of genes for zinc transporters in the susceptibility of clefting. PCRRFLP analysis was used to analyze single nucleotide polymorphisms of SLC30A1 (rs7526700, rs2278651, rs611386), SLC30A4 (rs2453531, rs8029246), SLC30A5 (rs351444, rs164393, rs6886492), SLC39A1 (rs10127484, rs11264736), and SLC39A3 (rs759071, rs4806874, rs10415622) in mothers of children with non-syndromic cleft lip with or without cleft palate (CL/P) and control mothers. The allele, genotype, and haplotype distribution was found to be similar among case and control mothers. Also, the gene-by-gene interaction analysis conducted using the Multifactor Dimensionality Reduction approach revealed no significant interactive genetic effect on having a child with a cleft. In conclusion, our results demonstrated that the analyzed polymorphic variants of genes for zinc transporters are not implicated in abnormal palatogenesis in the investigated group of women from the Polish population.