LociScan, a tool for screening genetic marker combinations for plant variety discrimination

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, Loci...

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Bibliographic Details
Published inThe Crop journal Vol. 12; no. 2; pp. 583 - 593
Main Authors Yang, Yang, Tian, Hongli, Yi, Hongmei, Shi, Zi, Wang, Lu, Fan, Yaming, Wang, Fengge, Zhao, Jiuran
Format Journal Article
LanguageEnglish
Published Beijing Elsevier B.V 01.04.2024
KeAi Publishing Communications Ltd
KeAi Communications Co., Ltd
Subjects
Algorithms
Chromosomes
Datasets
Discrimination
DNA fingerprinting
Fingerprinting
Genes
Genetic algorithm
Genetic algorithms
Genetic marker combination
Genetic markers
Genetic screening
Genomes
Genotype & phenotype
Germplasm
Mathematical analysis
Methods
Missing data
Optimization
Performance evaluation
Plant variety discrimination
Polymorphism
Population number
Screening
Software
Software utilities
Variety discrimination power
Plant variety discrimination
Genetic marker combination
Genetic algorithm
Variety discrimination power
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Summary: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.
ISSN:2214-5141
2095-5421
2214-5141
DOI:10.1016/j.cj.2024.01.001