LociScan, a tool for screening genetic marker combinations for plant variety discrimination
Yang Yang,
Hongli Tian,
Hongmei Yi,
Zi Shi,
Lu Wang,
Yaming Fan,
Fengge Wang,
Jiuran Zhao
Affiliations
Yang Yang
Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Hongli Tian
Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Hongmei Yi
Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Zi Shi
Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Lu Wang
Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Yaming Fan
Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Fengge Wang
Corresponding authors.; Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Jiuran Zhao
Corresponding authors.; Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Maize Research Institute, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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.
