Abstract
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.
Highlights
IntroductionGenome-wide association studies (GWAS) aim at identifying associations between Single Nucleotide Polymorphism (SNP) and disease, which has been an important way for identifying the genetic basis of diseases in the last decade [1,2,3,4,5,6,7,8,9,10,11]
Genome-wide association studies (GWAS) aim at identifying associations between Single Nucleotide Polymorphism (SNP) and disease, which has been an important way for identifying the genetic basis of diseases in the last decade [1,2,3,4,5,6,7,8,9,10,11].GWAS is capable of finding single-locus single nucleotide polymorphism (SNP) that is related to disease trait [7]
Gene Expression Programming (GEP)-EpiSeeker could be a promising alternative to the existing methods in epistasis detection and will provide a new way for accurately identifying epistasis
Summary
Genome-wide association studies (GWAS) aim at identifying associations between Single Nucleotide Polymorphism (SNP) and disease, which has been an important way for identifying the genetic basis of diseases in the last decade [1,2,3,4,5,6,7,8,9,10,11]. GWAS is capable of finding single-locus SNP that is related to disease trait [7]. Great progress has been made in identifying single-locus SNP that is the genetic causes of diseases such as Mendelian diseases and diabetes, detecting causative loci for complex diseases is more complicated [3, 5, 6, 12]. Identification of epistatic interactions provides a systematic way for exploring associations among different single nucleotide polymorphism (SNP) and complex diseases. 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
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