Genomics Inform.  2018 Dec;16(4):e33. 10.5808/GI.2018.16.4.e33.

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

• Jun I
• Choi W
• Park M

• KMID: 2439223
• DOI: http://doi.org/10.5808/GI.2018.16.4.e33

Abstract

Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed 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 a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending 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 the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks.

Keyword

data interpretation; generalized canonical correlation analysis; genome-wide association study; single nucleotide polymorphisms

MeSH Terms

Genome-Wide Association Study
Genotype
Phenotype
Polymorphism, Single Nucleotide