Centre of Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.
Gustavo Galindo Velasco Campus, Littoral Polytechnic Superior School (ESPOL), Guayaquil, Ecuador.
Sci Rep. 2021 Aug 3;11(1):15730. doi: 10.1038/s41598-021-95116-1.
Sugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV) is a major disease affecting sugarcane, a leading sugar and energy crop. Despite damages caused by SCYLV, the genetic base of resistance to this virus remains largely unknown. Several methodologies have arisen to identify molecular markers associated with SCYLV resistance, which are crucial for marker-assisted selection and understanding response mechanisms to this virus. We investigated the genetic base of SCYLV resistance using dominant and codominant markers and genotypes of interest for sugarcane breeding. A sugarcane panel inoculated with SCYLV was analyzed for SCYL symptoms, and viral titer was estimated by RT-qPCR. This panel was genotyped with 662 dominant markers and 70,888 SNPs and indels with allele proportion information. We used polyploid-adapted genome-wide association analyses and machine-learning algorithms coupled with feature selection methods to establish marker-trait associations. While each approach identified unique marker sets associated with phenotypes, convergences were observed between them and demonstrated their complementarity. Lastly, we annotated these markers, identifying genes encoding emblematic participants in virus resistance mechanisms and previously unreported candidates involved in viral responses. Our approach could accelerate sugarcane breeding targeting SCYLV resistance and facilitate studies on biological processes leading to this trait.
甘蔗黄叶病(SCYL)是一种由甘蔗黄叶病毒(SCYLV)引起的主要病害,严重影响甘蔗这一主要的糖料和能源作物。尽管 SCYLV 造成了损害,但对这种病毒的抗性遗传基础在很大程度上仍然未知。已经出现了几种方法来鉴定与 SCYLV 抗性相关的分子标记,这些标记对于标记辅助选择和理解对这种病毒的反应机制至关重要。我们使用显性和共显性标记以及感兴趣的甘蔗育种基因型来研究 SCYLV 抗性的遗传基础。用 SCYLV 接种的甘蔗小组分析了 SCYL 症状,并通过 RT-qPCR 估计了病毒滴度。该小组用 662 个显性标记和 70,888 个 SNP 和插入缺失标记进行了基因型分析,并具有等位基因比例信息。我们使用多倍体适应的全基因组关联分析和机器学习算法以及特征选择方法来建立标记-性状关联。虽然每种方法都确定了与表型相关的独特标记集,但它们之间存在一致性,证明了它们的互补性。最后,我们对这些标记进行了注释,鉴定了编码病毒抗性机制标志性参与者的基因和以前未报道的参与病毒反应的候选基因。我们的方法可以加速针对 SCYLV 抗性的甘蔗育种,并促进对导致这种特性的生物学过程的研究。