Genetic control and prospects of predictive breeding for European winter wheat’s Zeleny sedimentation values and Hagberg-Perten falling number

Author:

Muqaddasi Quddoos H.ORCID,Muqaddasi Roop KamalORCID,Ebmeyer Erhard,Korzun Viktor,Argillier Odile,Mirdita Vilson,Reif Jochen C.,Ganal Martin W.,Röder Marion S.ORCID

Abstract

Abstract Key message Sedimentation values and falling number in the last decades have helped maintain high baking quality despite rigorous selection for grain yield in wheat. Allelic combinations of major loci sustained the bread-making quality while improving grain yield. Glu-D1, Pinb-D1, and non-gluten proteins are associated with sedimentation values and falling number in European wheat. Abstract Zeleny sedimentation values (ZSV) and Hagberg-Perten falling number (HFN) are among the most important parameters that help determine the baking quality classes of wheat and, thus, influence the monetary benefits for growers. We used a published data set of 372 European wheat varieties evaluated in replicated field trials in multiple environments. ZSV and HFN traits hold a wide and significant genotypic variation and high broad-sense heritability. The genetic correlations revealed positive and significant associations of ZSV and HFN with each other, grain protein content (GPC) and grain hardness; however, they were all significantly negatively correlated with grain yield. Besides, GPC appeared to be the major predictor for ZSV and HFN. Our genome-wide association analyses based on high-quality SSR, SNP, and candidate gene markers revealed a strong quantitative genetic nature of ZSV and HFN by explaining their total genotypic variance as 41.49% and 38.06%, respectively. The association of known Glutenin (Glu-1) and Puroindoline (Pin-1) with ZSV provided positive analytic proof of our studies. We report novel candidate loci associated with globulins and albumins—the non-gluten monomeric proteins in wheat. In addition, predictive breeding analyses for ZSV and HFN suggest using genomic selection in the early stages of breeding programs with an average prediction accuracy of 81 and 59%, respectively.

Funder

Bundesministerium für Bildung und Forschung

Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK)

Publisher

Springer Science and Business Media LLC

Subject

Genetics,Agronomy and Crop Science,General Medicine,Biotechnology

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