Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

Abstract EN

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.