ArticleBMC bioinformatics2008
Using an evolutionary algorithm and parallel computing for haplotyping in a general complex pedigree with multiple marker loci.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Predicting unobserved phenotypes for complex traits from whole-genome SNP data.PLoS genetics · 2008Article
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3 authors.
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Abstract
backgroundHaplotype 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.
resultsAn 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 approximately 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 approximately 2 times. Overall, the proposed method with 4 processors increases the computational efficiency up to approximately 8 times compared to SimWalk2. The efficiency will increase more with a larger number of processors.
conclusionThe 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.
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