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

Overview of attention for article published in BMC Medical Genomics, April 2018
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Title
Fuzzy set-based generalized multifactor dimensionality reduction analysis of gene-gene interactions
Published in
BMC Medical Genomics, April 2018
DOI 10.1186/s12920-018-0343-0
Pubmed ID
Authors

Hye-Young Jung, Sangseob Leem, Taesung Park

Abstract

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. 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: Crohn's 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. 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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Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 60%
Student > Bachelor 1 20%
Unspecified 1 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 60%
Unspecified 1 20%
Psychology 1 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 April 2018.
All research outputs
#11,434,925
of 12,861,409 outputs
Outputs from BMC Medical Genomics
#551
of 618 outputs
Outputs of similar age
#235,114
of 270,038 outputs
Outputs of similar age from BMC Medical Genomics
#11
of 12 outputs
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