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

Abstract

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.

Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

“…Results were validated using simulated data and suggested superiority of the single-step method including both the intermediate omics and genomics data, over the traditional genomic best linear unbiased prediction (GBLUP) using only genomics data. Similar results were observed by Michel et al . (2021) when investigating the integration of gene expression into genomic prediction for disease resistance in wheat by using a hybrid relationship matrix for merging both layers of omics data.…”

“…2020; Christensen et al . 2021; Michel et al . 2021), such as transcriptomics, metabolomics, or microbiome data.…”

“…In this paper, we proposed the GTCBLUP model as an alternative to integrate genome and transcriptome data for genomic prediction. There has been an increasing interest in the use of intermediate omics data in animal and plant breeding (Guo et al 2016;Yang et al 2017;Azodi et al 2019;Morgante et al 2020;Christensen et al 2021;Michel et al 2021), such as transcriptomics, metabolomics, or microbiome data. The inclusion of new layers of omics data into genomic prediction models could arguably help in capturing additional portions of variance not explained by genotype data, but at the same time, these layers most likely contain overlapping information, increasing collinearity between predictors.…”

Associative and Physical Mapping of Markers Related to Fusarium in Maize Resistance, Obtained by Next-Generation Sequencing (NGS)

“…The SSR and STS markers were shown to be on chromosome 10 [ 43 ]. The use of SNP markers linked to the features of the yield structure in maize and barley showed greater precision than methods based on the study of metabolic pathways [ 44 ]. A useful tool for identifying candidate genes and their associated molecular markers is genome wide association studies (GWAS).…”

Fusarium Head Blight Resistance in European Winter Wheat: Insights From Genome-Wide Transcriptome Analysis

“…Fifteen of the experimental genotypes are offspring of 'Sumai3' or 'CM-82036' (Sumai3/Thornbird-S) that were phenotypically selected for their high resistance to FHB based on preceding experiments at IFA-Tulln, Austria. The panel was assessed for FHB severity in eld tests at IFA Tulln in 2014 and 2015 as described by Michel et al [39]. The wheat lines covered a broad range in FHB response from highly resistant to highly susceptible lines (Table S1).…”