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Using an evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple

Sang Hong Lee1, Julius H J Van der Werf, Brian P Kinghorn

  • 1The Institute for Genetics and Bioinformatics, School of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia. slee38@une.edu.au

BMC Bioinformatics
|April 15, 2008
PubMed
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An evolutionary algorithm offers a faster and more reliable method for haplotype reconstruction compared to simulated annealing. This approach improves computational efficiency for quantitative trait loci mapping.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Haplotype reconstruction is crucial for quantitative trait loci (QTL) mapping.
  • Simulated annealing (SA) is a common method but has limitations in convergence assessment and computational speed.

Purpose of the Study:

  • To introduce and evaluate an evolutionary algorithm (EA) for haplotype reconstruction.
  • To enhance computational efficiency and reliability in QTL mapping.

Main Methods:

  • Implementation of an evolutionary algorithm (EA) for haplotype reconstruction.
  • Utilizing parallel computing strategies with the EA.
  • Incorporating joint updating of dependent variables.

Main Results:

Related Experiment Videos

  • The EA demonstrates faster convergence and more reliable estimates than SA (SimWalk2).
  • Parallel processing with 4 processors resulted in an approximate 4-fold increase in speed.
  • Joint updating further improved efficiency by approximately 2-fold.
  • Overall, the EA with 4 processors achieved an 8-fold increase in computational efficiency compared to SimWalk2.

    • Conclusions:

    • The EA combined with joint updating is a promising tool for haplotype reconstruction.
    • This method significantly improves efficiency and reliability in linkage and association mapping of QTL.