Abstract
To reduce the cost and increase the efficiency of plant genetic marker fingerprinting for variety discrimination, it is desirable to identify the optimal marker combinations. We describe a marker combination screening model based on the genetic algorithm (GA) and implemented in a software tool, LociScan. Ratio-based variety discrimination power provided the largest optimization space among multiple fitness functions. Among GA parameters, an increase in population size and generation number enlarged optimization depth but also calculation workload. Exhaustive algorithm afforded the same optimization depth as GA but vastly increased calculation time. In comparison with two other software tools, LociScan accommodated missing data, reduced calculation time, and offered more fitness functions. In large datasets, the sample size of training data exerted the strongest influence on calculation time, whereas the marker size of training data showed no effect, and target marker number had limited effect on analysis speed.
