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Genomics Inform > Volume 16(4); 2018 > Article
DOI: https://doi.org/10.5808/GI.2018.16.4.e33    Published online December 28, 2018.
Multi-block Analysis of Genomic Data Using Generalized Canonical Correlation Analysis
Inyoung Jun1, Wooree Choi2, Mira Park3
1Department of Statistics, Korea University, Seoul, 02841, Korea
2Samsung Bioepis, Incheon, 21987, Korea
3Department of Preventive Medicine, Eulji University, Daejeon,34824, Korea
Corresponding author:  Mira Park
Tel: +82-42-259-1615   Fax: +82-10-9481-9110   Email: mira@eulji.ac.kr
Received: December 21, 2018   Revised: December 26, 2018   Accepted: December 26, 2018
Abstract
Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been studied to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is summation form of variable sets, is used for enhancing analysis of different block’s relationship. By identifying relationships through multi-block data form, we can understand the association between the blocks is effective in understanding the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from Korean Association Resource (KARE) project which has combination of the SNP blocks, phenotype blocks, and disease block.
Keywords: Multi-block analysis, generalized canonical correlation analysis, genome-wide association study
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