Identifying key genes for European canker resistance in apple: machine learning and gene expression profiling of quantitative disease resistance
Amanda Karlström, Antonio Gómez-Cortecero, John Connell, Charlotte Florence Nellist, Matthew Ordidge, Jim M. Dunwell, Richard Jonathan Harrison

TL;DR
This study identifies key genes involved in apple resistance to European canker using machine learning and gene expression analysis, offering insights for breeding disease-resistant apple varieties.
Contribution
The integration of transcriptomic profiling and machine learning to identify key genes and regulatory mechanisms for quantitative disease resistance in apple.
Findings
Genes involved in phenylpropanoid biosynthesis, immune receptors, and epigenetic regulation are linked to canker resistance.
Cis- and trans-regulatory effects were identified, revealing allele-dependent gene regulation.
Key expression patterns were validated in an independent dataset, confirming their robustness.
Abstract
European canker, caused by Neonectria ditissima, is a major disease of apple (Malus × domestica) with limited control options, making host resistance a key management strategy. Although quantitative disease resistance (QDR) has been identified, the underlying molecular basis remains poorly understood. We investigated candidate genes associated with resistance using transcriptomic profiling of a bi-parental population segregating for six QTLs linked to canker resistance. RNA sequencing combined with machine learning enabled the identification of key biomarkers predictive of disease resistance. Integration of expression and QTL data highlighted genes involved in phenylpropanoid biosynthesis, immune receptors (NLRs, RLKs, WAKs), and epigenetic regulators, implicating their roles in host defense. Expression patterns were further resolved into cis- and trans-regulatory effects, providing…
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Taxonomy
TopicsFungal Plant Pathogen Control · Plant-Microbe Interactions and Immunity · Plant Pathogens and Fungal Diseases
