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

Background Identification 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 in...

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Vydáno v:BMC genomics Ročník 22; číslo Suppl 1; s. 910 - 21
Hlavní autoři: Peng, Yu Zhong, Lin, Yanmei, Huang, Yiran, Li, Ying, Luo, Guangsheng, Liao, Jianping
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 20.12.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Adaptive control
Algorithms
Animal Genetics and Genomics
Bayes Theorem
Bayesian analysis
Biomedical and Life Sciences
Chi-square test
Chromosomes
Diabetes
Disease
Epistasis
Epistasis Analysis
Epistatic Interactions
Evolution
Evolutionary Algorithm
Fuzzy control
Gene Expression
Gene Expression Programming
Genetic algorithms
Genetic engineering
Genetic epistasis
Genetic research
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Heuristic
Life Sciences
Mathematical models
Medical genetics
Methods
Microarrays
Microbial Genetics and Genomics
Nucleotides
Optimization
Optimization algorithms
Performance evaluation
Plant Genetics and Genomics
Polymorphism
Polymorphism, Single Nucleotide
Programming
Proteomics
Single Nucleotide Polymorphisms
Single-nucleotide polymorphism
Statistical tests
China
Evolutionary Algorithm
Gene Expression Programming
Single Nucleotide Polymorphisms
Epistasis Analysis
Epistatic Interactions
ISSN:1471-2164, 1471-2164
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Shrnutí:Background Identification 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. Results In 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 F 1-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. Conclusions Here 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.
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ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-021-08207-8