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Isozyme Diversity in Wild Red Clover Populations from the Caucasus
Author(s) -
Mosjidis Jorge A.,
Greene Stephanie L.,
Klingler Kimberly A.,
Afonin Alexandr
Publication year - 2004
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2004.6650
Subject(s) - biology , isozyme , genetic diversity , germplasm , locus (genetics) , esterase , gene flow , population , allele , genetic variation , genetic variability , genetic distance , gene pool , genetics , zoology , botany , gene , genotype , biochemistry , demography , sociology , enzyme
The amount of genetic variation within species and populations and the distribution of genetic diversity (GD) among populations are important to breeders and biologists. The objective of this study was to assess GD on the basis of isozymes in 15 wild red clover ( Trifolium pratense L.) populations collected in the Caucasus Mountains, Russia. Isozymes assayed were esterase, β‐glucosidase, phosphoglucomutase, peroxidase, diaphorase, phosphoglucoisomerase, and superoxide dismutase. Ten isozyme loci with 26 alleles were detected. Ninety percent of the loci were polymorphic in at least one population. Percent polymorphic loci within populations ranged between 50 and 90%. At the species level, the number of alleles per polymorphic locus was 2.89 and effective number of alleles per locus was 1.84. Within‐population averages were 2.75 and 1.70, respectively. Genetic diversity was 0.353 at the species level and the mean value for the populations was 0.323. Some of the populations such as PI 604728 and PI 604735 were unique at the at the isozyme level. The other 13 accessions could be consolidated into one accession. Diversity measurements indicated that this species has a high level of variability that resides mostly within populations. Gene flow expectations based on predictions using topographic maps developed using interpolated digital and climatic surfaces coincided with gene flow estimates obtained using isozymes. This supports the use of GIS tools and remotely sensed data to develop maps that help germplasm collection and post‐collection efforts to understand patterns of GD in collected germplasm.