Multi-block Analysis of Genomic Data Using Generalized Canonical Correlation Analysis

• Published in: Genomics & informatics Vol. 16; no. 4; pp. e33 - 33
• Main Authors: Jun, Inyoung, Choi, Wooree, Park, Mira
• Format: Journal Article
• Language: English
• Published: Korea (South) Korea Genome Organization 01.12.2018; BioMed Central; 한국유전체학회
• Subjects: data interpretation; generalized canonical correlation analysis; genome-wide association study; Original; single nucleotide polymorphisms; 자연과학일반; Multi-block analysis; generalized canonical correlation analysis; genome-wide association study
• ISSN: 2234-0742; 1598-866X; 2234-0742
• DOI: 10.5808/GI.2018.16.4.e33

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.