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The Journal of Heredity 2001:92(5)
© 2001 The American Genetic Association 92:449-451

Computer Note

Parallel Computing in Interval Mapping of Quantitative Trait Loci

Ö. Carlborg, L. Andersson-Eklund, and L. Andersson

From the Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala Biomedical Center, Box 597, S-751 24 Uppsala, Sweden.

Address correspondence to Örjan Carlborg at the address above or e-mail: orjan.carlborg{at}hgen.slu.se.

Linear regression analysis is considered the least computationally demanding method for mapping quantitative trait loci (QTL). However, simultaneous search for multiple QTL, the use of permutations to obtain empirical significance thresholds, and larger experimental studies significantly increase the computational demand. This report describes an easily implemented parallel algorithm, which significantly reduces the computing time in both QTL mapping and permutation testing. In the example provided, the analysis time was decreased to less than 15% of a single processor system by the use of 18 processors. We indicate how the efficiency of the analysis could be improved by distributing the computations more evenly to the processors and how other ways of distributing the data facilitate the use of more processors. The use of parallel computing in QTL mapping makes it possible to routinely use permutations to obtain empirical significance thresholds for multiple traits and multiple QTL models. It could also be of use to improve the computational efficiency of the more computationally demanding QTL analysis methods.


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O. Carlborg, D. J. De Koning, K. F. Manly, E. Chesler, R. W. Williams, and C. S. Haley
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