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Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes
Joint Authors
Park, Taesung
Park, Mira
Kim, Hyein
Jeong, Hoe-Bin
Jung, Hye-Young
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Hyein& Jeong, Hoe-Bin& Jung, Hye-Young& Park, Taesung& Park, Mira. 2019. Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes. BioMed Research International،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1125433
Modern Language Association (MLA)
Kim, Hyein…[et al.]. Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes. BioMed Research International No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1125433
American Medical Association (AMA)
Kim, Hyein& Jeong, Hoe-Bin& Jung, Hye-Young& Park, Taesung& Park, Mira. Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1125433
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1125433