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

Cited 6 time in Web of Science Cited 7 time in Scopus
Authors

Jung, Hye-Young; Leem, Sangseob; Park, Taesung

Issue Date
2018-04-20
Publisher
BioMed Central
Citation
BMC Medical Genomics, 11(Suppl 2):32
Keywords
Gene-gene interactionFuzzy-set theoryFGMDRMultifactor dimensionality reduction
Abstract
Background
Gene-gene interactions (GGIs) are a known cause of missing heritability. Multifactor dimensionality reduction (MDR) is one of most commonly used methods for GGI detection. The generalized multifactor dimensionality reduction (GMDR) method is an extension of MDR method that is applicable to various types of traits, and allows covariate adjustments. Our previous Fuzzy MDR (FMDR) is another extension for overcoming simple binary classification. FMDR uses continuous member-ship values instead of binary membership values 0 and 1, improving power for detecting causal SNPs and more intuitive interpretations in real data analysis. Here, we propose the fuzzy generalized multifactor dimensionality reduction (FGMDR) method, as a combined analysis of fuzzy set-based analysis and GMDR method, to detect GGIs associated with diseases using fuzzy set theory.

Results
Through simulation studies for different types of traits, the proposed FGMDR showed a higher detection ratio of causal SNPs, compared to GMDR. We then applied FGMDR to two real data: Crohns disease (CD) data from the Wellcome Trust Case Control Consortium (WTCCC) with a binary phenotype and the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) data from Korean population with a continuous phenotype. The interactions derived by our method include the pre-reported interactions associated with phenotypes.

Conclusions
The proposed FGMDR performs well for GGI detection with covariate adjustments. The program written in R for FGMDR is available at http://statgen.snu.ac.kr/software/FGMDR

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ISSN
1755-8794
Language
English
URI
https://hdl.handle.net/10371/141242
DOI
https://doi.org/10.1186/s12920-018-0343-0
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