Seedling establishment is the first stage of crop productivity, and root phenotypes at seed emergence are critical to a successful start of shoot growth as well as for water and nutrient uptake. In this study, we investigate seedling establishment in winter wheat utilizing a newly developed workflow based on magnetic resonance imaging (MRI). Using the eight parents of the MAGIC (multi-parent advanced generation inter-cross) population we analysed the 4D root architecture of 288 individual seedlings grown in natural soils with plant neighbors over 3 d of development. Time of root and shoot emergence, total length, angle, and depth of the axile roots varied significantly among these genotypes. The temporal data resolved rates of elongation of primary roots and first and second seminal root pairs. Genotypes with slowly elongating primary roots had rapidly elongating first and second seminal root pairs and vice versa, resulting in variation in root system architecture mediated not only by root angle but also by initiation and relative elongation of axile roots. We demonstrated that our novel MRI workflow with a unique planting design and automated measurements allowed medium throughput phenotyping of wheat roots in 4D and could give new insights into regulation of root system architecture.

Crown rot (CR), caused by various Fusarium species, is a major disease in many cereal-growing regions worldwide. Fusarium culmorum is one of the most important species, which can cause significant yield losses in wheat. A set of 126 advanced International Maize and Wheat Improvement Center (CIMMYT) spring bread wheat lines were phenotyped against CR for field crown, greenhouse crown and stem, and growth room crown resistance scores. Of these, 107 lines were genotyped using Diversity Array Technology (DArT) markers to identify quantitative trait loci linked to CR resistance by genome-wide association study. Results of the population structure analysis grouped the accessions into three sub-groups. Genome wide linkage disequilibrium was large and declined on average within 20 cM (centi-Morgan) in the panel. General linear model (GLM), mixed linear model (MLM), and naïve models were tested for each CR score and the best model was selected based on quarantine-quarantine plots. Three marker-trait associations (MTAs) were identified linked to CR resistance; two of these on chromosome 3B were associated with field crown scores, each explaining 11.4% of the phenotypic variation and the third MTA on chromosome 2D was associated with greenhouse stem score and explained 11.6% of the phenotypic variation. Together, these newly identified loci provide opportunity for wheat breeders to exploit in enhancing CR resistance via marker-assisted selection or deployment in genomic selection in wheat breeding programs.

Root system architecture (RSA) is a target for breeding crops with effective nutrient and water use. Breeding can use populations designed to map quantitative trait loci (QTL). Here we non-invasively phenotype roots and leaves of the 16 foundation parents of two multi-parent advanced generation inter-cross (MAGIC) populations, covering diversity in spring (CSIRO MAGIC) and winter (NIAB MAGIC) wheats. RSA components varied after 16 days in the upgraded, paper-based imaging platform, GrowScreen-PaGe: lateral root length 2.2 fold; total root length, 1.9 fold; and seminal root angle 1.2 fold. RSA components total and lateral root length had the highest root heritabilities (H²) (H² = 0.4 for CSIRO and NIAB parents) and good repeatability (r = 0.7) in the GrowScreen-PaGe. These can be combined with leaf length (H² = 0.8 CSIRO; 0.7 NIAB) and number (H² = 0.6 CSIRO; 0.7 NIAB) to identify root and shoot QTL to breed for wheats with vigorous RSA and shoot growth at establishment, a critical phase for crop productivity. Time resolved phenotyping of MAGIC wheats also revealed parents to cross in future for growth rate traits (fastest: Robigus–NIAB and AC Barrie–CSIRO; slowest Rialto–NIAB and G204 Xiaoyan54–CSIRO) and root: shoot allocation traits (fast growers grew roots, notably laterals, quicker than leaves, compared to slow growers).

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

Root system architecture (RSA) is a target for breeding because of the interest to develop crops with roots that use nutrients and water more effectively. Breeding for RSA requires phenotypic diversity in populations amenable to QTL identification to provide markers for large breeding programs. This study examined the variation for root traits across the parents of two multi-parent advanced generation inter-cross (MAGIC) wheat populations from NIAB and CSIRO for 16 days in an upgraded version of the non-invasive, germination paper-based phenotyping platform, GrowScreen-PaGe . Across all parents, total root length varied up to 1.90 fold, root biomass 2.25 fold and seminal root angle 1.16 fold. The CSIRO parents grew faster, exhibited slightly wider seminal root angle and produced larger root systems compared to NIAB parents. Lateral root lengths, leaf lengths and biomass contrasted most between fastest (Robigus - NIAB and AC Barrie - CSIRO) and slowest growing parents (Rialto - NIAB and G204 Xiaoyan54 - CSIRO). Lengths of lateral and total root, and leaf number and length had moderate to high heritability (0.30-0.67) and repeatability. Lengths of lateral roots and leaves are good targets for enhancing wheat crop establishment, a critical stage for crop productivity.

Summary: Quantitative disease resistance is essential for durable crop protection, yet how numerous small-effect loci are organised across genomic and functional scales remains poorly understood. We investigated resistance to four distinct soil-borne pathogens in bread wheat ( Triticum aestivum ) by integrating genome-wide association mapping, linkage disequilibrium (LD)-defined hotspot analysis, comparative genomics, epistatic network analysis, haplotype analysis, functional annotation, transcriptomics and experimental evaluation. Resistance was highly polygenic and pathogen- and environment-dependent, with associated loci distributed across all three wheat subgenomes. Approximately two-thirds of hotspot components were shared among pathosystems, although evidence for excess sharing varied among comparisons and null models. Epistatic hubs were enriched within shared components, while interaction networks were dominated by interchromosomal and cross-subgenome interactions. Haplotype effects depended on local genomic context. Despite limited sharing of individual genes, convergence increased at the putative gene-family and broader defence-related functional levels. These findings support a hierarchical framework of quantitative resistance encompassing pathogen- and environment-dependent genomic architectures, partially shared genomic regions, local haplotypes and genetic interaction networks, with increasing convergence at broader functional levels. Limited transferability of individual marker associations in the independent panel further highlights the potential of multilocus breeding strategies integrating favourable haplotypes, recurrent genomic regions and diverse resistance mechanisms.