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

Publication ,  Journal Article
Larson, NB; Jenkins, GD; Larson, MC; Vierkant, RA; Sellers, TA; Phelan, CM; Schildkraut, JM; Sutphen, R; Pharoah, PPD; Gayther, SA; Goode, EL ...
Published in: Eur J Hum Genet
January 2014

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.

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Published In

Eur J Hum Genet

DOI

EISSN

1476-5438

Publication Date

January 2014

Volume

22

Issue

1

Start / End Page

126 / 131

Location

England

Related Subject Headings

  • Software
  • Risk Factors
  • Polymorphism, Single Nucleotide
  • Ovarian Neoplasms
  • NF-kappa B
  • Humans
  • High-Throughput Nucleotide Sequencing
  • Genetics & Heredity
  • Genetic Predisposition to Disease
  • Female
 

Citation

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Larson, N. B., Jenkins, G. D., Larson, M. C., Vierkant, R. A., Sellers, T. A., Phelan, C. M., … Fridley, B. L. (2014). Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer. Eur J Hum Genet, 22(1), 126–131. https://doi.org/10.1038/ejhg.2013.69
Larson, Nicholas B., Gregory D. Jenkins, Melissa C. Larson, Robert A. Vierkant, Thomas A. Sellers, Catherine M. Phelan, Joellen M. Schildkraut, et al. “Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.Eur J Hum Genet 22, no. 1 (January 2014): 126–31. https://doi.org/10.1038/ejhg.2013.69.
Larson NB, Jenkins GD, Larson MC, Vierkant RA, Sellers TA, Phelan CM, et al. Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer. Eur J Hum Genet. 2014 Jan;22(1):126–31.
Larson, Nicholas B., et al. “Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.Eur J Hum Genet, vol. 22, no. 1, Jan. 2014, pp. 126–31. Pubmed, doi:10.1038/ejhg.2013.69.
Larson NB, Jenkins GD, Larson MC, Vierkant RA, Sellers TA, Phelan CM, Schildkraut JM, Sutphen R, Pharoah PPD, Gayther SA, Wentzensen N, Ovarian Cancer Association Consortium, Goode EL, Fridley BL. Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer. Eur J Hum Genet. 2014 Jan;22(1):126–131.

Published In

Eur J Hum Genet

DOI

EISSN

1476-5438

Publication Date

January 2014

Volume

22

Issue

1

Start / End Page

126 / 131

Location

England

Related Subject Headings

  • Software
  • Risk Factors
  • Polymorphism, Single Nucleotide
  • Ovarian Neoplasms
  • NF-kappa B
  • Humans
  • High-Throughput Nucleotide Sequencing
  • Genetics & Heredity
  • Genetic Predisposition to Disease
  • Female