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Genetic differentiation and diversity of Callosobruchus chinensis collections from China
Callosobruchus chinensis (Linnaeus) is one of the most destructive pests of leguminous seeds. Genetic differentiation and diversity analysis of 345 C. chinensis individuals from 23 geographic populations using 20 polymorphic simple sequence repeats revealed a total of 149 alleles with an average of...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4762245/ https://www.ncbi.nlm.nih.gov/pubmed/26548842 http://dx.doi.org/10.1017/S0007485315000863 |
Sumario: | Callosobruchus chinensis (Linnaeus) is one of the most destructive pests of leguminous seeds. Genetic differentiation and diversity analysis of 345 C. chinensis individuals from 23 geographic populations using 20 polymorphic simple sequence repeats revealed a total of 149 alleles with an average of 7.45 alleles per locus. The average Shannon's information index was 1.015. The gene flow and genetic differentiation rate values at the 20 loci ranged from 0.201 to 1.841 and 11.0–47.2%, with averages of 0.849 and 24.4%, respectively. In the 23 geographic populations, the effective number of alleles and observed heterozygosity ranged from 1.441 to 2.218 and 0.191–0.410, respectively. Shannon's information index ranged from 0.357 to 0.949, with the highest value in Hohhot and the lowest in Rudong. In all comparisons, the fixation index (F(ST)) values ranged from 0.049 to 0.441 with a total F(ST) value of 0.254 among the 23 C. chinensis populations, indicating a moderate level of genetic differentiation and gene flow among these populations. Analysis of molecular variance revealed that the genetic variation within populations accounted for 76.7% of the total genetic variation. The genetic similarity values between populations varied from 0.617 to 0.969, whereas genetic distances varied from 0.032 to 0.483. Using unweighted pair-group method using arithmetical averages cluster analysis, the 23 geographic collections were classified into four distinct genetic groups but most of them were clustered into a single group. The pattern of the three concentrated groups from polymerase chain reactions analysis showed a somewhat different result with cluster. |
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