Identification of AFLP fragments linked to seed coat colour in Brassica juncea and conversion to a SCAR marker for rapid ... View Full Text


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Article Info

DATE

2000-07

AUTHORS

M. S. Negi, M. Devic, M. Delseny, M. Lakshmikumaran

ABSTRACT

A Brassica juncea mapping population was generated and scored for seed coat colour. A combination of bulked segregant analysis and AFLP methodology was employed to identify markers linked to seed coat colour in B. juncea. AFLP analysis using 16 primer combinations revealed seven AFLP markers polymorphic between the parents and the bulks. Individual plants from the segregating population were analysed, and three AFLP markers were identified as being tightly linked to the seed coat colour trait and specific for brown-seeded individuals. Since AFLP markers are not adapted for large-scale application in plant breeding, our objective was to develop a fast, cheap and reliable PCR-based assay. Towards this goal, we employed PCR-walking technology to isolate sequences adjacent to the linked AFLP marker. Based on the sequence information of the cloned flanking sequence of marker AFLP8, primers were designed. Amplification using the locus-specific primers generated bands at 0.5 kb and 1.2 kb with the yellow-seeded parent and a 1.1-kb band with the brown-seeded parent. Thus, the dominant AFLP marker (AFLP8) was converted into a simple codominant SCAR (Sequence Characterized Amplified Region) marker and designated as SCM08. Scoring of this marker in a segregating population easily distinguished yellow- and brown-seeded B. juncea and also differentiated between homozygous (BB) and heterozygous (Bb) brown-seeded individuals. Thus, this marker will be useful for the development of yellow seed B. juncea cultivars and facilitate the map-based cloning of genes responsible for seed coat colour trait. More... »

PAGES

146-152

Journal

TITLE

Theoretical and Applied Genetics

ISSUE

1-2

VOLUME

101

Related Patents

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s001220051463

DOI

http://dx.doi.org/10.1007/s001220051463

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029520776


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