Abstract

Molecular genetic markers can be used to examine a group of individuals or populations to estimate various diversity measures and genetic distances, infer population structure and clustering patterns, test for Hardy‐Weinberg and multilocus equilibrium, and test polymorphic loci for evidence of selective neutrality. This can be useful to plant breeders, germplasm managers, or others who are interested in population genetic properties of materials that they are working with. Many software programs for molecular population genetics studies have been developed for personal computers. Their easy access, implementation of sophisticated and powerful statistical techniques, and user‐friendliness make them an attractive alternative to performing calculations on spreadsheets or by writing simpler programs for oneself. This review outlines the current major features of six popular programs (TFPGA, Arlequin, GDA, GENEPOP, GeneStrut, and POPGENE), including the types of data they handle, analyses they perform, and where they can be obtained (Table 1). These particular programs were chosen because each can accommodate a variety of molecular marker types and perform many different types of analyses. Although there is much overlap in their functionality, each program has unique features to offer potential users.

Keywords

BiologyPopulationSoftwareTable (database)Variety (cybernetics)Cluster analysisGermplasmGenetic diversityPopulation geneticsMolecular markerData scienceComputational biologyComputer scienceGeneticsEvolutionary biologyData miningMachine learningArtificial intelligence

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Publication Info

Year
2000
Type
article
Volume
40
Issue
6
Pages
1521-1528
Citations
66
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

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66
OpenAlex
9
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Cite This

Joanne A. Labate (2000). Software for Population Genetic Analyses of Molecular Marker Data. Crop Science , 40 (6) , 1521-1528. https://doi.org/10.2135/cropsci2000.4061521x

Identifiers

DOI
10.2135/cropsci2000.4061521x

Data Quality

Data completeness: 77%