Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression. (5th August 2013)

• Record Type: Journal Article
• Authors: Zhang, Xuanping; Wang, Jiayin; Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi
• Other Names: Ferreira Eugénio Academic Editor.
• Is Part Of: BioMed research international. Volume 2013(2013)
• Publication Date: 2013-08-05
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2013/574735
• Languages: English
• ISSNs: 2314-6133

Abstract

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.