Oral Presentation Australasian Plant Pathology Society Conference 2025

Precise image-based phenotyping of barley foliar diseases enhances the identification and genetic characterisation of quantitative resistance (119807)

Matthew Ulrich 1 , Linda Brain 1 , Jianqiao Zhang 1 , Anthony Gendall 1 , Stefanie Lück 2 , Dimitar K Douchkov 2 , Hari Dadu 3 , Tara Garrard 4 , Eden Tongson 1 , Peter M Dracatos 1
  1. Department of Animal, Plant and Soil Sciences, La Trobe Institute for Sustainable Agriculture & Food (LISAF), La Trobe University, Bundoora, VIC, Australia
  2. Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Seeland, Germany
  3. Agriculture Victoria, Horsham, VIC 3401, Australia
  4. South Australian Research and Development Institute, Urrbrae, SA 5064, Australia

Host plant resistance is the most effective and environmentally sustainable means of reducing yield losses caused by cereal foliar pathogens 1–3. Genome-Wide Association Studies (GWAS) have emerged as the predominant association genetics technique, leading to the discovery of novel disease resistance genes and alleles in diverse panels of germplasm 4,5. GWAS requires two main inputs, (i) genotype data with sufficient marker density and (ii) phenotypic data of the disease response6,7. Disease resistance phenotyping is traditionally reliant on visual estimation of disease symptom severity, and this approach has successfully supported GWAS studies that have led to marker development and gene cloning efforts8–10.

Resistance expressed as a quantitative trait [known as quantitative resistance (QR)] is hypothesised to be non-race specific, more durable relative to major R genes, yet assessing QR visually is challenging, particularly when complicated by complex genotype × environment effects (G×E) in the field1,11–14.

To capture a broad range of phenotypic variation and enhance the detection frequency of QR in GWAS, phenotyping methods need to be accurate, unbiased, and quantitative.  The digital phenotyping platform comprising the Compolytics® Macrobot NextGen macro-imaging robot, and the Zeiss AxioScan 7 microscope slide scanner for fluorescence and bright field microscopy based at La Trobe University (AgriBio building) was developed to support advanced disease resistance phenotyping. This new research infrastructure at LTU is one of five GRDC-funded projects focussing on enhancing the genetic outcomes for Net Blotch Resistance (Net Blotch Consortium) in barley.

We aim to develop and apply image-based phenotyping at the macro- and microscopic scale using this specialised high-throughput image acquisition hardware coupled with quantitative image analysis software to mine available barley germplasm for resistance to various necrotrophic and biotrophic diseases.  Specifically, we aim to (i) to develop an image analysis module to quantitatively phenotype barley leaf rust and net blotch in a diverse panel of barley from Eastern Europe, and a population of Bowman near isogenic lines. Secondly, we aim to (ii) identify and characterise micro-phenotypic traits in key stages of fungal infection using the Zeiss AxioScan 7 microscope slide scanner; and finally (iii) perform a GWAS using these macro- and micro-phenotypic trait data and compare the results with multi-site field data.  This new approach of image-based quantitative disease phenotyping of cereal foliar pathogens aims to shift the selection bias away from major R-genes and enhance the efficacy and incorporation of quantitative disease resistance alleles into elite high-yielding varieties to enhance durability.

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