Detection for gene-gene co-association via kernel canonical correlation analysis
- Equal contributors
1 Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, 250012, China
2 Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine, 13125, Berlin, Germany
3 CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
4 Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, 200031, China
5 MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK
Citation and License
BMC Genetics 2012, 13:83 doi:10.1186/1471-2156-13-83Published: 8 October 2012
Currently, 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).
Through 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.
KCCU statistic is a valid and powerful gene-based method for detecting gene-gene co-association.