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Development and validation of donor-specific STS markers for tracking alien introgressions into Brassica juncea (L.) Czern
Alien introgressions into crop plants rely on phenotypic evaluation. Employing molecular markers could greatly accelerate this and help discover new alleles/QTLs. We report here a new strategy to develop markers for tracking introgression using genome survey sequence. We demonstrate this using an ad...
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Published in: | Molecular breeding 2017-09, Vol.37 (9), p.1-10, Article 110 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Alien introgressions into crop plants rely on phenotypic evaluation. Employing molecular markers could greatly accelerate this and help discover new alleles/QTLs. We report here a new strategy to develop markers for tracking introgression using genome survey sequence. We demonstrate this using an advanced backcross population of
Brassica juncea
involving the wild species
Diplotaxis erucoides
. To develop
D. erucoides
-specific markers, 72 million single end reads were obtained using Ion-Torrent platform. Quality reads (67.6 million) were checked against
Brassica
database and the redundant reads were eliminated.
De novo
assembly of the remaining 14.6 million reads gave 3895 contigs (> 1 kb), which were used to design 101 donor-specific (DS) STS markers. Of these, 89 markers showed polymorphism between
D. erucoides
and
B. juncea
. Genotyping of 90 randomly picked plants with 31 donor-specific STS markers detected 22 plants containing 17 markers. Alien introgression was also detected in eight of the 22 lines displaying phenotypes deviating from
B. juncea
parent. The marker DSSTS 70 was found in six of the nine lines showing glossy leaf suggesting its association with the trait. This is the first study demonstrating the use of molecular markers for implementing reverse genetics approach for alien introgression into crop plants. |
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ISSN: | 1380-3743 1572-9788 |
DOI: | 10.1007/s11032-017-0714-9 |