A Novel Two-Stage Multi-objective Ant Colony Optimization Approach for Epistasis Learning
Recently,genome-wide association study (GWAS) which aims to discover genetic effects in phenotypic traits is a hot issue in genetic epidemiology.Epistasis known as genetic interaction is an important challenge in GWAS since it explains most individual susceptibility to complex diseases and it is difficult to detect due to its non-linearity.Here we present a novel two-stage method based on multi-objective ant colony optimization for epistasis learning.We conduct a lot of experiments on a wide range of simulated datasets and compare the outcome of our method with some other recent epistasis learning methods like AntEpiSeeker,Bayesian epistasis association mapping (BEAM) and BOolean Operation-based Screening and Testing (BOOST) method,finding that our method has a high power and is time efficient to learn epistatic interactions.We also do experiments in the real Late-onset Alzheimer’s disease (LOAD) dataset and the results substantiate that our method has a potential in searching the suspicious epistasis in large scale real GWAS datasets.
Multi-objective Epistasis Genome-wide association study Single nucleotide polymorphism (SNP) Logistic regression Bayesian network Pareto optimal Ant colony optimization Chi-squared test
Pengjie Jing Hongbin Shen
Institute of Image Processing and Pattern Recognition,Shanghai Jiao Tong University,and Key Laboratory of System Control and Information Processing,Ministry of Education of China,Shanghai,200240,China
国际会议
Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)
长沙
英文
528-535
2014-11-01（万方平台首次上网日期，不代表论文的发表时间）