A novel fuzzy set based multifactor dimensionality reduction method for detecting gene–gene interaction
- Authors
- Jung,Hye-Young; Leem, Sangseob; Lee, Sungyoung; Park, Taesung
- Issue Date
- Dec-2016
- Publisher
- Pergamon Press Ltd.
- Keywords
- Fuzzy classifier; Fuzzy set theory; Gene-gene interaction; Multifactor dimensionality reduction; Uncertainty
- Citation
- Computational Biology and Chemistry, v.65, pp.193 - 202
- Indexed
- SCIE
SCOPUS
- Journal Title
- Computational Biology and Chemistry
- Volume
- 65
- Start Page
- 193
- End Page
- 202
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/15635
- DOI
- 10.1016/j.compbiolchem.2016.09.006
- ISSN
- 1476-9271
- Abstract
- Background Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. Methods We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Results Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. Conclusions We propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR. © 2016 Elsevier Ltd
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF SCIENCE AND CONVERGENCE TECHNOLOGY > ERICA 수리데이터사이언스학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/15635)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.