title: MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies.
abstract: BACKGROUND
  The existing methods for genetic-interaction detection in genome-wide association studies are designed from different paradigms, and their performances vary considerably for different disease models. One important reason for this variability is that their construction is based on a single-correlation model between SNPs and disease. Due to potential model preference and disease complexity, a single-objective method will therefore not work well in general, resulting in low power and a high false-positive rate.
  
  
  METHODS
  In this work, we present a multi-objective heuristic optimization methodology named MACOED for detecting genetic interactions. In MACOED, we combine both logistical regression and Bayesian network methods, which are from opposing schools of statistics. The combination of these two evaluation objectives proved to be complementary, resulting in higher power with a lower false-positive rate than observed for optimizing either objective independently. To solve the space and time complexity for high-dimension problems, a memory-based multi-objective ant colony optimization algorithm is designed in MACOED that is able to retain non-dominated solutions found in past iterations.
  
  
  RESULTS
  We compared MACOED with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method outperforms others in both detection power and computational feasibility for large datasets.
  
  
  METHODS
  Codes and datasets are available at: www.csbio.sjtu.edu.cn/bioinf/MACOED/.
authors.affiliation: 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 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.
authors.forename: Peng-Jie
authors.initials: PJ
authors.lastname: Jing
authors.affiliation: 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 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.
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D000818:Animals; D001000:Ants / Q000235:genetics*; D001499:Bayes Theorem; D004843:Epistasis, Genetic / Q000235:genetics*; D016678:Genome; D055106:Genome-Wide Association Study; D016015:Logistic Models; D008954:Models, Biological; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*