ArticleEuropean journal of human genetics : EJHG2014
Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer.
Article in European journal of human genetics : EJHG, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 38 citations in OpenAlex.
- Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease.NPJ systems biology and applications · 2024Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies.Frontiers in cell and developmental biology · 2021Article
- Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.Frontiers in genetics · 2021Article
- Multi-group analysis using generalized additive kernel canonical correlation analysis.Scientific reports · 2020Article
- Unsupervised discovery of phenotype-specific multi-omics networks.Bioinformatics (Oxford, England) · 2019Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Article
- Haplotype-based interaction of the PPARGC1A and UCP1 genes is associated with impaired fasting glucose or type 2 diabetes mellitus.Medicine · 2017Observational
- Eigen-Epistasis for detecting gene-gene interactions.BMC bioinformatics · 2017Article
- Joint sparse canonical correlation analysis for detecting differential imaging genetics modules.Bioinformatics (Oxford, England) · 2016Article
- A gene-based information gain method for detecting gene-gene interactions in case-control studies.European journal of human genetics : EJHG · 2015Article
- Identification of modules related to programmed cell death in CHD based on EHEN.BioMed research international · 2014Article
- A kernel regression approach to gene-gene interaction detection for case-control studies.Genetic epidemiology · 2013Article
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Authors and funding
14 authors at 7 institutions in 2 countries.
Funding
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.
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