GEP-EpiSeeker: a gene expression programming-based method for epistatic interaction detection in genome-wide association studies

BMC Genomics

Ying Li¹  Jianping Liao¹  Yanmei Lin¹  Yu Zhong Peng²  Yiran Huang³  Guangsheng Luo⁴ 

[1] School of Computer & Information Engineering, Nanning Normal University, 530001, Nanning, China;School of Computer & Information Engineering, Nanning Normal University, 530001, Nanning, China;School of Computer science, Fudan University, 200433, Shanghai, China;School of Computer and Electronics and Information, Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, 530004, Nanning, China;School of Computer science, Fudan University, 200433, Shanghai, China;

关键词: Gene Expression Programming;    Epistatic Interactions;    Epistasis Analysis;    Single Nucleotide Polymorphisms;    Evolutionary Algorithm;   

DOI  :  10.1186/s12864-021-08207-8

来源: Springer

【 摘 要 】

BackgroundIdentification of epistatic interactions provides a systematic way for exploring associations among different single nucleotide polymorphism (SNP) and complex diseases. Although considerable progress has been made in epistasis detection, efficiently and accurately identifying epistatic interactions remains a challenge due to the intensive growth of measuring SNP combinations.ResultsIn this work, we formulate the detection of epistatic interactions by a combinational optimization problem, and propose a novel evolutionary-based framework, called GEP-EpiSeeker, to detect epistatic interactions using Gene Expression Programming. In GEP-EpiSeeker, we propose several tailor-made chromosome rules to describe SNP combinations, and incorporate Bayesian network-based fitness evaluation into the evolution of tailor-made chromosomes to find suspected SNP combinations, and adopt the Chi-square test to identify optimal solutions from suspected SNP combinations. Moreover, to improve the convergence and accuracy of the algorithm, we design two genetic operators with multiple and adjacent mutations and an adaptive genetic manipulation method with fuzzy control to efficiently manipulate the evolution of tailor-made chromosomes. We compared GEP-EpiSeeker with state-of-the-art methods including BEAM, BOOST, AntEpiSeeker, MACOED, and EACO in terms of power, recall, precision and F1-score on the GWAS datasets of 12 DME disease models and 10 DNME disease models. Our experimental results show that GEP-EpiSeeker outperforms comparative methods.ConclusionsHere we presented a novel method named GEP-EpiSeeker, based on the Gene Expression Programming algorithm, to identify epistatic interactions in Genome-wide Association Studies. The results indicate that GEP-EpiSeeker could be a promising alternative to the existing methods in epistasis detection and will provide a new way for accurately identifying epistasis.

【 授权许可】

CC BY