Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions

The Impact of Cardiovascular Disease Gene Polymorphism and Interaction with Homocysteine on Deep Vein Thrombosis

Deep vein thrombosis (DVT) affects vascular health and can even threaten life; however, its pathogenesis remains unclear. Cardiovascular disease (CVD) and DVT share common risk factors, such as dyslipidemia, aging, etc. We aimed to investigate the loci of published CVD susceptibility genes and their association with environmental factors that might be related to DVT. Genotyping by Kompetitive Allele Specific PCR (KASP), collection of lifestyle information, and determination of blood biochemical markers were performed in 165 DVT cases and 164 controls. The impact of six single nucleotide polymorphisms (SNPs) and additional potential variables on DVT morbidity was evaluated using unconditional logistic regression (ULR). To explore the high-order interactions related to genetics and the body's internal environment exposure that affect DVT, ULR, crossover analysis, and multifactor dimensionality reduction/generalized multifactor dimensionality reduction (MDR/GMDR) were employed. Sensitivity analyses were performed using the EpiR package. The polymorphisms of FGB rs1800790 and PLAT rs2020918 were significantly associated with DVT. The optimum GMDR interaction model for gene-gene (G × G) consisted of THBD rs1042579, PLAT rs2020918, and PON1 rs662. The PLAT rs2020918 and MTHFR rs1801133 polymorphisms together eliminated the maximum entropy by the MDR method. The optimum GMDR interaction model for gene-environment (G × E) consisted of MTHFR rs1801133, FGB rs1800790, PLAT rs2020918, PON1 rs662, and total homocysteine (tHcy). Those with high tHcy levels and three risk genotypes significantly increased the DVT risk. In conclusion, certain CVD-related SNPs and their interactions with tHcy may contribute to DVT. These have implications for investigating DVT etiology and developing preventive treatment plans.

Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic

Among the set of parameters for which data are collected for decision-making based on artificial intelligence methods, often only some of the parameters are significant. This article compares methods for determining the significant parameters based on the theory of mathematical statistics, and fuzzy and boolean logic. The testing was conducted on several test data sets with a different number of parameters and different variability of parameter values. It was shown that for data sets with a small number of parameters (<5), the most accurate result was given for a method based on the theory of mathematical statistics and boolean logic. For a data set with a large number of parameters—the most suitable is the method of fuzzy logic.

An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis

OBJECTIVE

Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research.

METHODS AND MATERIAL

In this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification. The FS method combined with MDR measurements yielded an improved ability to distinguish similar frequencies of potential multifactor genotypes.

RESULTS

We compared our results with other MDR-based methods and FSMDR achieved superior detection rates on simulated data sets. The results indicated that the fuzzy classifications can provide insight into the uncertainty of H/L classification in MDR operation.

CONCLUSION

FSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set.

Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes

To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multifactor dimensionality reduction (MDR). Early MDR methods mainly focused on binary traits. More recently, several extensions of MDR have been developed for analyzing various traits such as quantitative traits and survival times. Newer technologies, such as genome-wide association studies (GWAS), have now been developed for assessing multiple traits, to simultaneously identify genetic variants associated with various pathological phenotypes. It has also been well demonstrated that analyzing multiple traits has several advantages over single trait analysis. While there remains a need to find GGIs for multiple traits, such studies have become more difficult, due to a lack of novel methods and software. Herein, we propose a novel multi-CMDR method, by combining fuzzy clustering and MDR, to find GGIs for multiple traits. Multi-CMDR showed similar power to existing methods, when phenotypes followed bivariate normal distributions, and showed better power than others for skewed distributions. The validity of multi-CMDR was confirmed by analyzing real-life Korean GWAS data.

EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution

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.