Abstract
The location of quantitative trait loci (QTLs) in a linkage map and their genetic effects are estimated using the recombination fraction between QTLs and the neighboring flanking markers. The existence of QTLs is evaluated using the log likelihood value (called the LOD score). Since the LOD score drops at the positions of the markers, numerical optimizations based on local structure of the log likelihood function do not work. Usually, LOD scores are calculated for all possible locations of QTLs. This procedure is impractical, particularly when existence of multiple QTLs is suspected and we do not know the number of contributing loci. Here, we propose a genetic algorithm (GA) which takes the number and the locations of QTLs as its “genotype”. We adopt Akaike’s information criterion (AIC) as “fitness” to avoid false positive QTLs. Numerical experiments clearly showed that our GA, unlike other commonly used procedures such as interval mapping, estimated the number and locations of QTLs precisely. Strikingly, it detected two loci which are located on an interval between a pair of neighboring markers could be detected separately.
