Plant Pathology, University of Florida, Gainesville, FL, USA.
Horticultural Sciences Department, University of Florida, Gainesville, FL, USA.
Theor Appl Genet. 2024 Aug 6;137(8):196. doi: 10.1007/s00122-024-04698-7.
Integrating disease screening data and genomic data for host and pathogen populations into prediction models provides breeders and pathologists with a unified framework to develop disease resistance. Developing disease resistance in crops typically consists of exposing breeding populations to a virulent strain of the pathogen that is causing disease. While including a diverse set of pathogens in the experiments would be desirable for developing broad and durable disease resistance, it is logistically complex and uncommon, and limits our capacity to implement dual (host-by-pathogen)-genome prediction models. Data from an alternative disease screening system that challenges a diverse sweet corn population with a diverse set of pathogen isolates are provided to demonstrate the changes in genetic parameter estimates that result from using genomic data to provide connectivity across sparsely tested experimental treatments. An inflation in genetic variance estimates was observed when among isolate relatedness estimates were included in prediction models, which was moderated when host-by-pathogen interaction effects were incorporated into models. The complete model that included genomic similarity matrices for host, pathogen, and interaction effects indicated that the proportion of phenotypic variation in lesion size that is attributable to host, pathogen, and interaction effects was similar. Estimates of the stability of lesion size predictions for host varieties inoculated with different isolates and the stability of isolates used to inoculate different hosts were also similar. In this pathosystem, genetic parameter estimates indicate that host, pathogen, and host-by-pathogen interaction predictions may be used to identify crop varieties that are resistant to specific virulence mechanisms and to guide the deployment of these sources of resistance into pathogen populations where they will be more effective.
将疾病筛查数据和宿主及病原体群体的基因组数据整合到预测模型中，为种植者和病理学家提供了一个统一的框架，以开发疾病抗性。在作物中开发疾病抗性通常包括使育种群体暴露于引起疾病的病原体的强毒菌株中。虽然在实验中包含多种病原体对于开发广泛和持久的疾病抗性是理想的，但从逻辑上讲这很复杂且不常见，并且限制了我们实施双（宿主-病原体）基因组预测模型的能力。提供了来自替代疾病筛查系统的数据，该系统用多种病原体分离物挑战多样化的甜玉米群体，以证明使用基因组数据提供实验处理之间的连接性会导致遗传参数估计值发生变化。当在预测模型中包含分离物亲缘关系估计值时，观察到遗传方差估计值膨胀，当将宿主-病原体相互作用效应纳入模型时，这种膨胀得到缓解。包含宿主、病原体和相互作用效应的基因组相似性矩阵的完整模型表明，病变大小表型变异的比例归因于宿主、病原体和相互作用效应是相似的。接种不同分离物的宿主品种的病变大小预测的稳定性以及用于接种不同宿主的分离物的稳定性的估计也相似。在该病理系统中，遗传参数估计表明，宿主、病原体和宿主-病原体相互作用的预测可以用于识别对特定毒力机制具有抗性的作物品种，并指导将这些抗性来源部署到病原体群体中，在这些群体中它们将更有效。
