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

Year: 2013
Type: article
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 approach to detect gene-gene interactions on the basis of a kernelized version of CCA (KCCA). Our simulation studies showed that KCCA controls the Type-I error, and is more powerful than leading gene-based approaches under a disease model with negligible marginal effects. To demonstrate the utility of our approach, we also applied KCCA to assess interactions between 200 genes in the NF-κB pathway in relation to ovarian cancer risk in 3869 cases and 3276 controls. We identified 13 significant gene pairs relevant to ovarian cancer risk (local false discovery rate <0.05). Finally, we discuss the advantages of KCCA in gene-gene interaction analysis and its future role in genetic association studies.
DOI: 10.1038/ejhg.2013.69
Authors: Nicholas B Larson, Gregory D Jenkins, Melissa C Larson, Robert A Vierkant, Thomas A Sellers, Catherine M Phelan, Joellen M Schildkraut, Rebecca Sutphen, Paul P D Pharoah, Simon A Gayther, Nicolas Wentzensen, Ellen L Goode, Brooke L Fridley
Institutions: Mayo Clinic, Moffitt Cancer Center, Duke University, Duke Medical Center, University of South Florida, University of Cambridge, University of Southern California, National Cancer Institute, Division of Cancer Epidemiology and Genetics, University of Kansas Medical Center
Language: English
Subfield: Genetics
Open Access status: hybrid
Estimated APC paid: $2,790
Funders: Mayo Foundation for Medical Education and Research, Fred C. and Katherine B. Andersen Foundation, Cancer Research UK, National Institutes of Health, NIHR Cambridge Biomedical Research Centre, National Cancer Institute, National Institute of General Medical Sciences
Awards: G1440585907, Bayesian hierarchical nonlinear models for pharmacogenomic cytotoxicity studies, G2613824078, G2899816875, Population-Based Analysis of Ovarian Cancer Biomarkers, G4061461591, G4513596833, Integrative genomic models for analysis of pharmacogenomic studies, G4713782523, G5389006787, G6179720485, G6283257732, Follow-up of Ovarian Cancer Genetic Association and Interaction Studies (FOCI), Genetic Variation in the NF-kappaB Pathway and Ovarian Cancer Etiology, The Mitochondrial Genome and Ovarian Cancer Risk, G862694305, Haplotype-Based Genome Screen for Ovarian Cancer Loci, G9000230084, G9000248580, G9000301350, Mayo Clinic Comprehensive Cancer Center Support Grant, Moffitt Cancer Center Support Grant, Mayo Clinic Ovarian Cancer SPORE, The University of Kansas Cancer Center
OpenAlex ID: W2041713290
FWCI: 3.096
Cites: 36
Cited by: 39
Related to: 10