Methodology article

Evolutionary algorithms for the selection of single nucleotide polymorphisms

Robert M Hubley¹ , Eckart Zitzler² and Jared C Roach¹

¹Institute for Systems Biology, Seattle, WA, USA

²Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Zurich, Switzerland

author email corresponding author email

BMC Bioinformatics 2003, 4:30doi:10.1186/1471-2105-4-30

Published:	23 July 2003

Abstract

Background

Large databases of single nucleotide polymorphisms (SNPs) are available for use in genomics studies. Typically, investigators must choose a subset of SNPs from these databases to employ in their studies. The choice of subset is influenced by many factors, including estimated or known reliability of the SNP, biochemical factors, intellectual property, cost, and effectiveness of the subset for mapping genes or identifying disease loci. We present an evolutionary algorithm for multiobjective SNP selection.

Results

We implemented a modified version of the Strength-Pareto Evolutionary Algorithm (SPEA2) in Java. Our implementation, Multiobjective Analyzer for Genetic Marker Acquisition (MAGMA), approximates the set of optimal trade-off solutions for large problems in minutes. This set is very useful for the design of large studies, including those oriented towards disease identification, genetic mapping, population studies, and haplotype-block elucidation.

Conclusion

Evolutionary algorithms are particularly suited for optimization problems that involve multiple objectives and a complex search space on which exact methods such as exhaustive enumeration cannot be applied. They provide flexibility with respect to the problem formulation if a problem description evolves or changes. Results are produced as a trade-off front, allowing the user to make informed decisions when prioritizing factors. MAGMA is open source and available at http://snp-magma.sourceforge.net webcite. Evolutionary algorithms are well suited for many other applications in genomics.