Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

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 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.

Time-course expression QTL-atlas of the global transcriptional response of wheat to Fusarium graminearum.

Fusarium head blight is a devastating disease of small grain cereals such as bread wheat (Triticum aestivum). The pathogen switches from a biotrophic to a nectrotrophic lifestyle in course of disease development forcing its host to adapt its defence strategies. Using a genetical genomics approach, we illustrate genome-wide reconfigurations of genetic control over transcript abundances between two decisive time points after inoculation with the causative pathogen Fusarium graminearum. Whole transcriptome measurements have been recorded for 163 lines of a wheat doubled haploid population segregating for several resistance genes yielding 15 552 at 30 h and 15 888 eQTL at 50 h after inoculation. The genetic map saturated with transcript abundance-derived markers identified of a novel QTL on chromosome 6A, besides the previously reported QTL Fhb1 and Qfhs.ifa-5A. We find a highly different distribution of eQTL between time points with about 40% of eQTL being unique for the respective assessed time points. But also for more than 20% of genes governed by eQTL at either time point, genetic control changes in time. These changes are reflected in the dynamic compositions of three major regulatory hotspots on chromosomes 2B, 4A and 5A. In particular, control of defence-related biological mechanisms concentrated in the hotspot at 4A shift to hotspot 2B as the disease progresses. Hotspots do not colocalize with phenotypic QTL, and within their intervals no higher than expected number of eQTL was detected. Thus, resistance conferred by either QTL is mediated by few or single genes.