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Abstract

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.

Published in

Journal of Heredity
2001, volume: 92, number: 5, pages: 449-451
Publisher: OXFORD UNIV PRESS INC

SLU Authors

UKÄ Subject classification

Genetics and Breeding in Agricultural Sciences

Publication identifier

  • DOI: https://doi.org/10.1093/jhered/92.5.449

Permanent link to this page (URI)

https://res.slu.se/id/publ/57921