PDF(Pubmed)
Abstract：
Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss.
摘要：
菌核病菌(Ss)是最具破坏性的真菌病原体之一。在包括油菜在内的多种经济重要作物中造成巨大的产量损失。植物对Ss的抗性与由多个次要基因控制的定量抗病性（QDR）有关。涉及QDR至Ss的基因的全基因组鉴定尚未进行。在这项研究中,我们整合了几种检测方法，包括全基因组关联研究(GWAS)，多组学共定位，和机器学习预测来识别，在全基因组范围内，涉及油菜QDR到Ss的基因。采用GWAS和多组学共定位，我们确定了与油菜对Ss的抗性相关的七个抗性相关基因座（RALs）。此外，我们开发了一种机器学习算法，并将其命名为综合多组学分析和目标基因预测机器学习（iMAP），它整合了多组学数据，以快速预测广泛染色体区域内的疾病抗性相关基因。通过基于识别RAL的iMAP，我们揭示了与SsQDR相关的多个钙信号基因。对变异的选择性扫描和单倍型的群体水平分析证实了进化过程中预测的钙信号基因的阳性选择。总的来说,这项研究开发了一种集成了多组数据和机器学习方法的算法，为预测与特定性状相关的靶基因提供了强有力的工具。此外，为进一步了解钙信号基因在SsQDR中的作用和机制奠定了基础。
