Literature DB >> 31144000

An experimental approach for estimating the genomic selection advantage for Fusarium head blight and Septoria tritici blotch in winter wheat.

Cathérine Pauline Herter1, Erhard Ebmeyer2, Sonja Kollers2, Viktor Korzun2, Thomas Miedaner3.   

Abstract

KEY MESSAGE: The genomic selection advantage for Fusarium head blight is promising but failed for Septoria tritici blotch. Selection of new breeding parents based on predictions must be treated with caution. Genomic selection (GS) is an approach that uses whole-genome marker data to estimate breeding values of untested genotypes and holds the potential to improve the genetic gain in breeding programs by shortening the breeding cycle and increasing the selection intensity. However, reported realized gain from genomic selection is limited to few experiments. In this study, a training population of 1120 winter wheat lines derived from 14 bi-parental families was genotyped with genome-wide single nucleotide polymorphism markers and phenotyped for Fusarium head blight (FHB) and Septoria tritici blotch (STB) severity, plant height and heading date. We used weighted ridge regression best linear unbiased prediction to calculate genomic estimated breeding values (GEBVs) of 2500 genotypes. Based on GEBVs, we selected the most resistant individuals as well as a random sample and tested them in a multi-location field trial. We computed moderate coefficients of correlation between observed and predicted trait values for FHB severity, plant height and heading date and achieved a genomic selection advantage of 10.62 percentage points for FHB resistance compared to the randomly chosen subpopulation. Genomic selection failed for the improvement of STB resistance with a genomic selection advantage of only 2.14 percentage points. Our results also indicate that the selection of new breeding parents based on GEBVs must be treated with caution. Taken together, the implementation of GS for FHB resistance, plant height and heading date seems to be promising. For traits with very strong genotype × environment variance, like STB resistance, GS appears to be still challenging.

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Year:  2019        PMID: 31144000     DOI: 10.1007/s00122-019-03364-7

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  48 in total

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3.  Parental selection, number of breeding populations, and size of each population in inbred development.

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4.  Genomic-assisted prediction of genetic value with semiparametric procedures.

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Authors:  M E Goddard; B J Hayes
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7.  Stacking quantitative trait loci (QTL) for Fusarium head blight resistance from non-adapted sources in an European elite spring wheat background and assessing their effects on deoxynivalenol (DON) content and disease severity.

Authors:  T Miedaner; F Wilde; B Steiner; H Buerstmayr; V Korzun; E Ebmeyer
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8.  Molecular mapping of Fusarium head blight resistance in the winter wheat population Dream/Lynx.

Authors:  M Schmolke; G Zimmermann; H Buerstmayr; G Schweizer; T Miedaner; V Korzun; E Ebmeyer; L Hartl
Journal:  Theor Appl Genet       Date:  2005-06-10       Impact factor: 5.699

Review 9.  Strategies for managing Fusarium head blight and deoxynivalenol accumulation in wheat.

Authors:  Gary Y Yuen; Susan D Schoneweis
Journal:  Int J Food Microbiol       Date:  2007-07-31       Impact factor: 5.277

10.  Identification of QTLs for resistance to Fusarium head blight, DON accumulation and associated traits in the winter wheat variety Arina.

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Journal:  Theor Appl Genet       Date:  2007-07-03       Impact factor: 5.699

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Review 2.  Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.

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Review 5.  Climate change will influence disease resistance breeding in wheat in Northwestern Europe.

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6.  Genetic Mapping and Prediction Analysis of FHB Resistance in a Hard Red Spring Wheat Breeding Population.

Authors:  Yuan Liu; Evan Salsman; Jason D Fiedler; Justin B Hegstad; Andrew Green; Mohamed Mergoum; Shaobin Zhong; Xuehui Li
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7.  Gains through selection for grain yield in a winter wheat breeding program.

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Review 8.  Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize.

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9.  Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat.

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  9 in total

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