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BioMed Research International
Volume 2013 (2013), Article ID 574735, 11 pages
http://dx.doi.org/10.1155/2013/574735
Research Article

Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

1Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China
2The Genome Institute, Washington University, St. Louis, MO 63108, USA
3College of Medicine and Forensics, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi 710061, China
4Key Laboratory of the Ministry of Health for Forensic Sciences, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, China
5Key Laboratory of the Ministry of Education for Environment and Genes Related to Diseases, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, China
6Center for Translational Medicine, The First Affiliated Hospital of Xi’an Jiaotong University College of Medicine, Xi’an, Shaanxi 710061, China

Received 2 April 2013; Revised 8 June 2013; Accepted 2 July 2013

Academic Editor: Eugénio Ferreira

Copyright © 2013 Xuanping Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds.