Assessing genotypic softness in single wheat kernels using starch granule-associated friabilin as a biochemical marker View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1995-01

AUTHORS

A. D. Bettge, C. F. Morris, G. A. Greenblatt

ABSTRACT

The end-use quality of wheat (Triticum aestivum L.) is determined in large part by the texture of the grain (soft or hard). Endosperm texture is currently determined by several empirical methods. These methods are limited because the use bulk grain lots, as opposed to individual kernels; assess phenotypic, as opposed to genotypic hardness; require a quantity of grain greater than that generally available in the early generations of wheat breeding programs, and are destructive. Recent approaches that use single kernels address the problems associated with bulk grain lots, but suffer the other limitations of providing only the phenotype and being destructive. An objective method for determining the texture genotype of single kernels of wheat was developed using starch granule-associated friabilin, a family of closely related 15 kDa proteins, as a biochemical marker. The occurrence of friabilin on water-washed wheat starch granules is apparently unaffected by the environment and is perfectly correlated (no exceptions) with grain softness. The technique presented here can detect friabilin on as little as 0.2 mg of starch and provides a 250-fold improvement in friabilin detection compared to previous methods. The method is capable of correctly assessing the genotype of F1 heterozygotes from hard x soft and soft x hard crosses. Further, the method uses only a portion of the endosperm from the kernel and therefore accommodates embryo propagation and high molecular weight glutenin subunit characterization. This single kernel method also facilitates the genetic characterization of mixed, bulk grain lots. More... »

PAGES

65-72

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00035940

DOI

http://dx.doi.org/10.1007/bf00035940

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1017859970


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