ArticleBMC genetics2012
Detection for gene-gene co-association via kernel canonical correlation analysis.
Article in BMC genetics, 2012. 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, 28 citations in OpenAlex.
- cLD: Rare-variant linkage disequilibrium between genomic regions identifies novel genomic interactions.PLoS genetics · 2023Article
- Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches.Frontiers in plant science · 2021Article
- 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
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Article
- A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia.Journal of neuroscience methods · 2018Article
- Genetic-Epigenetic Interactions in Asthma Revealed by a Genome-Wide Gene-Centric Search.Human heredity · 2018Article
- 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
- Article
- A gene-based information gain method for detecting gene-gene interactions in case-control studies.European journal of human genetics : EJHG · 2015Article
- A kernel regression approach to gene-gene interaction detection for case-control studies.Genetic epidemiology · 2013Article
- Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrently, most methods for detecting gene-gene interaction (GGI) in genomewide association studies (GWASs) are limited in their use of single nucleotide polymorphism (SNP) as the unit of association. One way to address this drawback is to consider higher level units such as genes or regions in the analysis. Earlier we proposed a statistic based on canonical correlations (CCU) as a gene-based method for detecting gene-gene co-association. However, it can only capture linear relationship and not nonlinear correlation between genes. We therefore proposed a counterpart (KCCU) based on kernel canonical correlation analysis (KCCA).
resultsThrough simulation the KCCU statistic was shown to be a valid test and more powerful than CCU statistic with respect to sample size and interaction odds ratio. Analysis of data from regions involving three genes on rheumatoid arthritis (RA) from Genetic Analysis Workshop 16 (GAW16) indicated that only KCCU statistic was able to identify interactions reported earlier.
conclusionsKCCU statistic is a valid and powerful gene-based method for detecting gene-gene co-association.
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