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Article
2020

Genomics-assisted breeding for quantitative disease resistances in small-grain cereals and maize

Abstract

Generating genomics-driven knowledge opens a way to accelerate the resistance breeding process by family or population mapping and genomic selection. Important prerequisites are large populations that are genomically analyzed by medium- to high-density marker arrays and extensive phenotyping across locations and years of the same populations. The latter is important to train a genomic model that is used to predict genomic estimated breeding values of phenotypically untested genotypes. After reviewing the specific features of quantitative resistances and the basic genomic techniques, the possibilities for genomics-assisted breeding are evaluated for six pathosystems with hemi-biotrophic fungi: Small-grain cereals/Fusarium head blight (FHB), wheat/Septoria tritici blotch (STB) and Septoria nodorum blotch (SNB), maize/Gibberella ear rot (GER) and Fusarium ear rot (FER), maize/Northern corn leaf blight (NCLB). Typically, all quantitative disease resistances are caused by hundreds of QTL scattered across the whole genome, but often available in hotspots as exemplified for NCLB resistance in maize. Because all crops are suffering from many diseases, multi-disease resistance (MDR) is an attractive aim that can be selected by specific MDR QTL. Finally, the integration of genomic data in the breeding process for introgression of genetic resources and for the improvement within elite materials is discussed.

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International journal of molecular sciences, 21 (1996), 24. https://doi.org/10.3390/ijms21249717. ISSN: 1422-0067
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English

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630 Agriculture

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@article{Miedaner2020, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16597}, doi = {10.3390/ijms21249717}, author = {Miedaner, Thomas and Boeven, Ana Luisa Galiano-Carneiro and Gaikpa, David Sewodor et al.}, title = {Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize}, journal = {International journal of molecular sciences}, year = {2020}, volume = {21}, number = {24}, }