Tag SNP selection via a genetic algorithm

  • Authors:

  • Ghasem Mahdevar;Javad Zahiri;Mehdi Sadeghi;Abbas Nowzari-Dalini;Hayedeh Ahrabian

  • Affiliations:

  • Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran;Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran;National Institute of Genetic Engineering and Biotechnology, Tehran, Iran;Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran and Center of Excellence in Biomathematics, School of Mathematics, Statistics, and Comput ...;Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran and Center of Excellence in Biomathematics, School of Mathematics, Statistics, and Comput ...

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2010

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Abstract

Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.