Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

Genomic selection with genome-wide distributed molecular markers has evolved into a well-implemented tool in many breeding programs during the last decade. The resistance against Fusarium head blight (FHB) in wheat is probably one of the most thoroughly studied systems within this framework. Aside f...

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Bibliographic Details
Published in:Genes 2021-01, Vol.12 (1), p.114
Main Authors: Michel, Sebastian, Wagner, Christian, Nosenko, Tetyana, Steiner, Barbara, Samad-Zamini, Mina, Buerstmayr, Maria, Mayer, Klaus, Buerstmayr, Hermann
Format: Article
Language:English
Subjects:
Cultivars
Disease resistance
Disease Resistance - genetics
Fusarium
Fusarium - pathogenicity
Fusarium head blight
Gene expression
Genome, Plant
Genomes
Genomics
Genotype & phenotype
Genotypes
Humidity
Metabolism
Metabolites
Plant breeding
Plant Breeding - methods
Plant Diseases - genetics
Plant Diseases - microbiology
Plant resistance
Predictions
Principal components analysis
Ribonucleic acid
RNA
RNA-Seq
Selection, Genetic
Transcriptomes
Transcriptomics
Triticum - genetics
Triticum - microbiology
Wheat
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Summary:Genomic selection with genome-wide distributed molecular markers has evolved into a well-implemented tool in many breeding programs during the last decade. The resistance against Fusarium head blight (FHB) in wheat is probably one of the most thoroughly studied systems within this framework. Aside from the genome, other biological strata like the transcriptome have likewise shown some potential in predictive breeding strategies but have not yet been investigated for the FHB-wheat pathosystem. The aims of this study were thus to compare the potential of genomic with transcriptomic prediction, and to assess the merit of blending incomplete transcriptomic with complete genomic data by the single-step method. A substantial advantage of gene expression data over molecular markers has been observed for the prediction of FHB resistance in the studied diversity panel of breeding lines and released cultivars. An increase in prediction ability was likewise found for the single-step predictions, although this can mostly be attributed to an increased accuracy among the RNA-sequenced genotypes. The usage of transcriptomics can thus be seen as a complement to already established predictive breeding pipelines with pedigree and genomic data, particularly when more cost-efficient multiplexing techniques for RNA-sequencing will become more accessible in the future.
ISSN:2073-4425
2073-4425
DOI:10.3390/genes12010114