Identifying key genes for European canker resistance in apple: machine learning and gene expression profiling of quantitative disease resistance

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 insight into allele-dependent regulation. Independent validation in a separate dataset confirmed the robustness of key expression patterns. These findings advance understanding of the genetic architecture underlying QDR in apple and provide a basis for marker development to support breeding of cultivars with durable resistance to European canker.