Barcode System for Genetic Identification of Soybean [Glycine max (L.) Merrill] Cultivars Using InDel Markers Specific to Dense Variation Blocks.
Front Plant Sci
; 8: 520, 2017.
Article
en En
| MEDLINE
| ID: mdl-28443113
ABSTRACT
For genetic identification of soybean [Glycine max (L.) Merrill] cultivars, insertions/deletions (InDel) markers have been preferred currently because they are easy to use, co-dominant and relatively abundant. Despite their biological importance, the investigation of InDels with proven quality and reproducibility has been limited. In this study, we described soybean barcode system approach based on InDel makers, each of which is specific to a dense variation block (dVB) with non-random recombination due to many variations. Firstly, 2,274 VBs were mined by analyzing whole genome data in six soybean cultivars (Backun, Sinpaldal 2, Shingi, Daepoong, Hwangkeum, and Williams 82) for transferability to dVB-specific InDel markers. Secondly, 73,327 putative InDels in the dVB regions were identified for the development of soybean barcode system. Among them, 202 dVB-specific InDels from all soybean cultivars were selected by gel electrophoresis, which were converted as 2D barcode types according to comparing amplicon polymorphisms in the five cultivars to the reference cultivar. Finally, the polymorphism of the markers were assessed in 147 soybean cultivars, and the soybean barcode system that allows a clear distinction among soybean cultivars is also detailed. In addition, the changing of the dVBs in a chromosomal level can be quickly identified due to investigation of the reshuffling pattern of the soybean cultivars with 27 maker sets. Especially, a backcross-inbred offspring, "Singang" and a recurrent parent, "Sowon" were identified by using the 27 InDel markers. These results indicate that the soybean barcode system enables not only the minimal use of molecular markers but also comparing the data from different sources due to no need of exploiting allele binning in new varieties.
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MEDLINE
Tipo de estudio:
Diagnostic_studies
Idioma:
En
Revista:
Front Plant Sci
Año:
2017
Tipo del documento:
Article