Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.

ABSTRACT: Although single-locus approaches have been widely applied to identify disease-associated single-nucleotide polymorphisms (SNPs), complex diseases are thought to be the product of multiple interactions between loci. This has led to the recent development of statistical methods for detecting statistical interactions between two loci. Canonical correlation analysis (CCA) has previously been proposed to detect gene-gene coassociation. However, this approach is limited to detecting linear relations and can only be applied when the number of observations exceeds the number of SNPs in a gene. This limitation is particularly important for next-generation sequencing, which could yield a large number of novel variants on a limited number of subjects. To overcome these limitations, we propose an appr

SUBMITTER: Larson NB 

PROVIDER: S-EPMC3865403 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature