Background: Genome-wide association study (GWAS) plays a very important role in identifying the causes of disease. Due to most of the existing methods for genetic-interaction detection in GWAS are designed for a single-correlation model, their performances vary considerably for different disease models. These methods usually have high computation cost and low accuracy.
Method: We present a new multi-objective heuristic optimization methodology named HS-MMGKG for detecting genetic interactions. In HS-MMGKG, we use harmony search with five objective functions to improve the efficiency and accuracy. A new strategy based on p-value and MDR is adopt to generate more reasonable results. The Boolean representation in BOOST is modified to calculate the five functions rapidly. These strategies result in less time complexity and higher accuracy while detecting the potential models.
Results: We compare HS-MMGKG with CSE, MACOED and FHSA-SED using 26 simulated datasets. The experimental results demonstrate that our method outperforms the others in accuracy and computation time. Our method has identified many two-locus SNP combinations that may be associated with seven diseases in WTCCC dataset. Some of the SNPs have direct evidence in CTD database. The results may be helpful to further explain the pathogenesis.
Conclusion: It is anticipated that our proposed algorithm could be used to in GWAS which is helpful for understanding of disease mechanism, diagnosis and prognosis.