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

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.