Title: Gene-gene interaction analysis for quantitative trait using cluster-based multifactor dimensionality reduction method
Authors: Youjung Lee; Hyein Kim; Taesung Park; Mira Park
Addresses: Department of Statistics, Korea University, Seoul, South Korea ' Department of Statistics, Korea University, Seoul, South Korea ' Department of Statistics, Seoul National University, Seoul, South Korea ' Department of Preventive Medicine, Eulji University, Daejon, South Korea
Abstract: With recent advances in high-throughput genotyping techniques, many genome-wide association studies have been conducted to understand the relationship between genes and complex diseases. Though single SNP analysis is common for many genetic studies, this approach has a limitation in explaining genetic changes in complex diseases. Most complex diseases cannot be explained by a single gene mutation, and lack of success in many genetic studies could be attributed to gene-gene interactions. Although various methods have been developed to identify gene-gene interactions for binary traits, few statistical methods are currently available for determining the genetic interactions associated with quantitative traits. To address this problem, we propose CL-MDR method. It is a modified version of multifactor dimensionality reduction for quantitative traits. The proposed method was examined by simulation studies, which showed that CL-MDR successfully identified interactions associated with quantitative traits. We have also applied our approach to a Korean GWAS data for illustration.
Keywords: clustering; genetic associations; gene-gene interactions; multifactor dimensionality reduction; quantitative trait.
International Journal of Data Mining and Bioinformatics, 2018 Vol.20 No.1, pp.1 - 11
Available online: 26 May 2018 *Full-text access for editors Access for subscribers Free access Comment on this article