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CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies
Cheng-Hong Yang1,2, Li-Yeh Chuang3, Yu-Da Lin1
1Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan.
Bioinformatics (Oxford, England)
|April 6, 2017
Summary
A new algorithm, DECMDR, efficiently detects epistatic interactions in genome-wide association studies (GWAS). It improves detection success rates and computational efficiency for identifying complex genetic associations.
Area of Science:
- Genetics
- Computational Biology
- Bioinformatics
Background:
- Detecting epistatic interactions in genome-wide association studies (GWAS) presents significant computational challenges due to the vast number of single-nucleotide polymorphism (SNP) combinations.
- Existing algorithms are limited in their ability to analyze large-scale SNP datasets for potential epistasis.
Purpose of the Study:
- To develop and evaluate a novel algorithm, DECMDR, for efficient detection of epistatic interactions in GWAS.
- To overcome the computational limitations of analyzing extensive SNP datasets.
Main Methods:
- Proposed a new algorithm, DECMDR, combining the differential evolution (DE) algorithm with classification-based multifactor-dimensionality reduction (CMDR).
- Utilized CMDR as a fitness measure within the DE process to scan for statistical epistasis in GWAS.
Main Results:
- DECMDR demonstrated superior performance compared to existing algorithms in terms of detection success rate.
- The algorithm showed efficiency in analyzing all possible SNP combinations to detect significant associations between cases and controls.
- Results validated through large-scale simulations and real data from the Wellcome Trust Case Control Consortium.
Conclusions:
- DECMDR offers an effective and efficient solution for detecting epistatic interactions in GWAS.
- The algorithm enhances the ability to identify complex genetic associations in large datasets.
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