Genetic characterization of flour quality and bread-making traits in a spring wheat nested association mapping population

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Improving grain protein content and flour quality is an important goal for wheat (Triticum aestivum L.) breeding programs. Identification of genomic regions and markers associated with quality traits can help enrich wheat breeding germplasm with favorable alleles. This study was carried out to identify genetic loci controlling bread-making quality traits in a nested association mapping population of 230 lines. Lines were evaluated for grain protein content and three gluten aggregation traits from the Brabender GlutoPeak instrument: peak maximum time (PMT), maximum torque (MT), and aggregation energy (AE). The strongest positive correlation was observed between AE and MT (r =.87), whereas the strongest negative correlation was between PMT and MT (r = −.46). We identified 25 quantitative trait loci (QTL) associated with these traits on 14 chromosomes, of which three QTL were common between MT and AE. Quantitative trait loci explaining the largest variations were observed for grain protein content and AE, with QTL that explained 13% of the observed variation for each trait. Nearly 62% of the discovered QTL were of medium effect, as the median percentage of observed phenotypic variation explained by the detected QTL was 7% (range of 4–13%). Based on QTL analysis and marker screening results, the populations discussed in this study are segregating for the known alleles of the high-molecular-weight glutenin gene Glu-D1. As some lines have better phenotypic performance relative to the elite common parent RB07, crossing these lines with elite breeding lines can help increase the frequency of alleles contributing to improve flour quality traits.

Original languageEnglish (US)
Pages (from-to)1168-1183
Number of pages16
JournalCrop Science
Issue number2
StatePublished - Jan 12 2021

Bibliographical note

Funding Information:
We thank Susan Reynolds, Oadi Matny, and Saddeka Zeada for technical assistance in the field and seed processing. We are also very grateful for the support from the University of Minnesota Agricultural Experiment Stations and the Minnesota Supercomputing Institute. This research was supported by the Minnesota Wheat Research and Promotion Council and the United States Agency for International Development to the University of California, Davis (Award no. 201400223‐13 amendment 2 NCE).

Publisher Copyright:
© 2020 The Authors. Crop Science © 2020 Crop Science Society of America


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