biostudies-literatureLarson NBCancer Research UKNCI NIH HHSNIGMS NIH HHS126-31https://www.ebi.ac.uk/biostudies/studies/S-EPMC3865403biostudies-literatureUnknown22(1)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-?B 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.European journal of human genetics : EJHGKernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.PMC386540316561CA106414R21 GM086689R01 CA149429P30 CA168524R01 CA106414CA114343R01 CA122443C490/A10119P50 CA136393CA168524P30 CA015083U19-CA148112R01 CA114343CA122443R21 CA140879U19 CA14811210124GM86689CA136393P30 CA076292CA140879Schildkraut JMJenkins GDGayther SAOvarian Cancer Association ConsortiumLarson NBVierkant RASutphen RWentzensen NLarson MCPhelan CMPharoah PPFridley BLSellers TAGoode ELfalseKernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.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-?B 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.2014-01-01T00:00:00Z2014 Jan2021-02-21T03:58:08Z2019-03-27T01:18:34ZS-EPMC38654032359140410.1038/ejhg.2013.69