Genetics, Vol. 155, 2003-2010, August 2000, Copyright © 2000

The Use of a Genetic Algorithm for Simultaneous Mapping of Multiple Interacting Quantitative Trait Loci

Örjan Carlborga, Leif Anderssona, and Brian Kinghornb
a Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala Biomedical Center, S-751 24 Uppsala, Sweden
b Department of Animal Science, University of New England, Armidale, New South Wales 2351, Australia

Corresponding author: Örjan Carlborg, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala Biomedical Ctr., Box 597, S-751 24 Uppsala, Sweden., orjan.carlborg{at}hgen.slu.se (E-mail)

Communicating editor: C. HALEY

Here we describe a general method for improving computational efficiency in simultaneous mapping of multiple interacting quantitative trait loci (QTL). The method uses a genetic algorithm to search for QTL in the genome instead of an exhaustive enumerative ("step-by-step") search. It can be used together with any method of QTL mapping based on a genomic search, since it only provides a more efficient way to search the genome for QTL. The computational demand decreases by a factor of ~130 when using genetic algorithm-based mapping instead of an exhaustive enumerative search for two QTL in a genome size of 2000 cM using a resolution of 1 cM. The advantage of using a genetic algorithm increases further for larger genomes, higher resolutions, and searches for more QTL. We show that a genetic algorithm-based search has efficiency higher than or equal to a search method conditioned on previously identified QTL for all epistatic models tested and that this efficiency is comparable to that of an exhaustive search for multiple QTL. The genetic algorithm is thus a powerful and computationally tractable alternative to the exhaustive enumerative search for simultaneous mapping of multiple interacting QTL. The use of genetic algorithms for simultaneous mapping of more than two QTL and for determining empirical significance thresholds using permutation tests is also discussed.




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