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

Lee, Sang Hong, Van der Werf, Julius H. J. and Kinghorn, Brian P. (2008) Using an evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker loci. BMC Bioinformatics, 9 189.1-189.10. doi:10.1186/1471-2105-9-189


Author Lee, Sang Hong
Van der Werf, Julius H. J.
Kinghorn, Brian P.
Title Using an evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker loci
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2008-04-01
Year available 2008
Sub-type Article (original research)
DOI 10.1186/1471-2105-9-189
Open Access Status DOI
Volume 9
Start page 189.1
End page 189.10
Total pages 10
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: Haplotype reconstruction is important in linkage mapping and association mapping of quantitative trait loci (QTL). One widely used statistical approach for haplotype reconstruction is simulated annealing (SA), implemented in SimWalk2. However, the algorithm needs a very large number of sequential iterations, and it does not clearly show if convergence of the likelihood is obtained.


Results: An evolutionary algorithm (EA) is a good alternative whose convergence can be easily assessed during the process. It is feasible to use a powerful parallel-computing strategy with the EA, increasing the computational efficiency. It is shown that the EA can be ∼4 times faster and gives more reliable estimates than SimWalk2 when using 4 processors. In addition, jointly updating dependent variables can increase the computational efficiency up to ∼2 times. Overall, the proposed method with 4 processors increases the computational efficiency up to ∼8 times compared to SimWalk2. The efficiency will increase more with a larger number of processors.

Conclusion: The use of the evolutionary algorithm and the joint updating method can be a promising tool for haplotype reconstruction in linkage and association mapping of QTL.
Keyword Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
Biochemistry & Molecular Biology
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
BIOCHEMICAL RESEARCH METHODS
BIOTECHNOLOGY & APPLIED MICROBIOLOGY
MATHEMATICAL & COMPUTATIONAL BIOLOGY
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Brain Institute Publications
 
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