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Detection for gene-gene co-association via kernel canonical correlation analysis
Zhongshang Yuan1, Qingsong Gao, Yungang He
1Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, 250012, China.
BMC Genetics
|October 9, 2012
Summary
Kernel Canonical Correlation (KCCA) based statistic (KCCU) effectively detects gene-gene interactions, outperforming previous methods in identifying complex genetic associations for diseases like rheumatoid arthritis.
Area of Science:
- Genetics
- Biostatistics
- Computational Biology
Background:
- Current gene-gene interaction (GGI) detection in genome-wide association studies (GWASs) often relies on single nucleotide polymorphisms (SNPs), limiting analysis.
- Gene-based or region-based analyses offer broader insights into genetic associations.
- Previous canonical correlation (CCU) methods captured only linear gene-gene co-associations.
Purpose of the Study:
- To introduce a novel gene-based statistic, KCCU, utilizing kernel canonical correlation analysis (KCCA).
- To address the limitation of detecting only linear relationships by incorporating nonlinear correlations between genes.
- To evaluate the performance of KCCU compared to existing methods.
Main Methods:
- Development of the KCCU statistic based on KCCA for gene-based association analysis.
- Simulation studies to assess the validity and power of KCCU.
- Application of KCCU to real-world genetic data, specifically rheumatoid arthritis (RA) data from GAW16.
Main Results:
- Simulation results demonstrated KCCU as a valid statistical test.
- KCCU showed increased power compared to the CCU statistic regarding sample size and interaction odds ratio.
- KCCU successfully identified previously reported gene interactions in RA data, while CCU did not.
Conclusions:
- The KCCU statistic is a valid and powerful gene-based method for detecting gene-gene co-associations.
- KCCU offers an advantage in capturing both linear and nonlinear genetic interactions.
- This method enhances the ability to identify complex genetic architectures underlying diseases.
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