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A genetic ensemble approach for gene-gene interaction identification.
Pengyi Yang1, Joshua W K Ho, Albert Y Zomaya
1School of Information Technologies, University of Sydney, NSW 2006, Australia. yangpy7@gmail.com
BMC Bioinformatics
|October 22, 2010
Summary
This study introduces a novel genetic ensemble algorithm for identifying complex gene-gene and gene-environment interactions. The new method shows comparable and complementary results to existing approaches, improving complex disease research.
Area of Science:
- Genetics
- Computational Biology
- Bioinformatics
Background:
- Complex diseases arise from gene-gene and gene-environment interactions.
- Identifying these genetic interactions is a significant challenge in disease research.
Purpose of the Study:
- To propose a novel hybrid algorithm for identifying gene-gene and gene-environment interactions.
- To convert SNP interaction identification into a combinatorial feature selection data mining problem.
Main Methods:
- A hybrid algorithm combining a genetic algorithm (GA) with an ensemble of classifiers (genetic ensemble).
- Utilizing GA to explore SNP subsets and environmental factors for interaction pattern extraction.
- Employing a combinatorial ranking method and a novel formula based on pairwise double fault to quantify complementarity.
Main Results:
- The genetic ensemble algorithm demonstrates comparable identification power to Multifactor Dimensionality Reduction (MDR) and outperforms Polymorphism Interaction Analysis (PIA).
- Results from the genetic ensemble are highly complementary to those from PIA and MDR.
- Simulation studies and real-world data applications confirm the algorithm's effectiveness.
Conclusions:
- The proposed genetic ensemble algorithm is effective for identifying gene-gene and gene-environment interactions.
- Combining identification results from different algorithms, including the genetic ensemble, offers potential benefits.
- This approach enhances the accuracy and comprehensiveness of genetic interaction analysis in complex diseases.
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