Identification of fusarium head blight resistance markers in a genome-wide association study of CIMMYT spring synthetic hexaploid derived wheat lines

Serajazari, Mitra; Torkamaneh, Davoud; Gordon, Emily; Lee, Elizabeth; Booker, Helen; Pauls, K. Peter; Navabi, Alireza

doi:10.1186/s12870-023-04306-8

Cited by 3 publications

(2 citation statements)

References 87 publications

(94 reference statements)

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“…Plant resistance to FHB is quantitative, and effective in-field phenotyping is necessary to identify the many small effect loci that confer meaningful resistance (Serajazari et al ., 2023). The symptoms of the disease can often be mistaken for normal plant maturity, which makes phenotyping subjective and increases rater variation.…”

Section: Introductionmentioning

confidence: 99%

“…Research groups use different rating methods to capture disease variability and host response. By averaging the number of infected kernels from multiple individual spikes or giving a 0-100% disease severity score to a random sample of spikes in a plot, researchers are able to limit subjectivity, but time and labor requirements are high (Huang et al ., 2018; Serajazari et al ., 2023). In contrast, assigning a 0-100% aggregate disease severity based on a full plot visual inspection reduces time and labor, but introduces greater human bias to disease scoring (Talas et al ., 2012).…”

Section: Introductionmentioning

confidence: 99%

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An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat

Cooper,

Du,

Beaver

et al. 2023

Preprint

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Fusarium head blight (FHB) in wheat is an economically important disease, which can cause yield losses exceeding 50% and the causal pathogen that infects spikes produces harmful mycotoxins. Breeding for host resistance remains the most effective disease control method; but time, labor, and human subjectivity during disease scoring limits selection advancements. In this study we describe an innovative, high-throughput phenotyping rover for capturing in-field RGB images and a deep neural network pipeline for wheat spike detection and FHB disease quantification. The image analysis pipeline successfully detects wheat spikes from images under variable field conditions, segments spikes and diseased tissue in the spikes, and quantifies disease severity as the region of intersection between spike and disease masks. Model inferences on an individual spike and plot basis were compared to human visual disease scoring in the field and on imagery for model evaluation. The precision and throughput of the model surpassed traditional field rating methods. The accuracy of FHB severity assessments of the model was equivalent to human disease annotations of images, however individual spike disease assessment was influenced by field location. The model was able to quantify FHB in images taken with different camera orientations in an unseen year, which demonstrates strong generalizability. This innovative pipeline represents a breakthrough in FHB phenotyping, offering precise and efficient assessment of FHB on both individual spikes and plot aggregates. The model is robust to different conditions and the potential to standardize disease evaluation methods across the community make it a valuable tool for studying and managing this economically significant fungal disease.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat

Cooper,

Du,

Beaver

et al. 2023

Preprint

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Identification of candidate genes for Fusarium head blight resistance from QTLs using RIL population in wheat

Zhang,

Jiang,

et al. 2024

Plant Mol Biol

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Molecular Investigations to Improve Fusarium Head Blight Resistance in Wheat: An Update Focusing on Multi-Omics Approaches

Sirangelo

2024

Plants

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Fusarium head blight (FHB) is mainly caused by Fusarium graminearum (Fg) and is a very widespread disease throughout the world, leading to severe damage to wheat with losses in both grain yield and quality. FHB also leads to mycotoxin contamination in the infected grains, being toxic to humans and animals. In spite of the continuous advancements to elucidate more and more aspects of FHB host resistance, to date, our knowledge about the molecular mechanisms underlying wheat defense response to this pathogen is not comprehensive, most likely due to the complex wheat–Fg interaction. Recently, due to climate changes, such as high temperature and heavy rainfall, FHB has become more frequent and severe worldwide, making it even more urgent to completely understand wheat defense mechanisms. In this review, after a brief description of the first wheat immune response to Fg, we discuss, for each FHB resistance type, from Type I to Type V resistances, the main molecular mechanisms involved, the major quantitative trait loci (QTLs) and candidate genes found. The focus is on multi-omics research helping discover crucial molecular pathways for each resistance type. Finally, according to the emerging examined studies and results, a wheat response model to Fg attack, showing the major interactions in the different FHB resistance types, is proposed. The aim is to establish a useful reference point for the researchers in the field interested to adopt an interdisciplinary omics approach.

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Identification of fusarium head blight resistance markers in a genome-wide association study of CIMMYT spring synthetic hexaploid derived wheat lines

Cited by 3 publications

References 87 publications

An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat

An RGB based deep neural network for high fidelity Fusarium head blight phenotyping in wheat

Identification of candidate genes for Fusarium head blight resistance from QTLs using RIL population in wheat

Molecular Investigations to Improve Fusarium Head Blight Resistance in Wheat: An Update Focusing on Multi-Omics Approaches

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