Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA.
Department of Plant Pathology, North Dakota State University, Fargo, ND, 58108, USA.
Sci Rep. 2021 Nov 5;11(1):21773. doi: 10.1038/s41598-021-01272-9.
Sclerotinia stem rot (SSR) is a fungal disease of rapeseed/canola that causes significant seed yield losses and reduces its oil content and quality. In the present study, the reaction of 187 diverse canola genotypes to SSR was characterized at full flowering stage using the agar plug to stem inoculation method in four environments. Genome-wide association study (GWAS) using three different algorithms identified 133 significant SNPs corresponding with 123 loci for disease traits like stem lesion length (LL), lesion width (LW), and plant mortality at 14 (PM_14D) and 21 (PM_21D) days. The explained phenotypic variation of these SNPs ranged from 3.6 to 12.1%. Nineteen significant SNPs were detected in two or more environments, disease traits with at least two GWAS algorithms. The strong correlations observed between LL and other three disease traits evaluated, suggest they could be used as proxies for SSR resistance phenotyping. Sixty-nine candidate genes associated with disease resistance mechanisms were identified. Genomic prediction (GP) analysis with all the four traits employing genome-wide markers resulted in 0.41-0.64 predictive ability depending on the model specifications. The highest predictive ability for PM_21D with three models was about 0.64. From our study, the identified resistant genotypes and stable significant SNP markers will serve as a valuable resource for future SSR resistance breeding. Our study also suggests that genomic selection holds promise for accelerating canola breeding progress by enabling breeders to select SSR resistance genotypes at the early stage by reducing the need to phenotype large numbers of genotypes.
菌核茎腐病(SSR)是一种影响油菜/芥花的真菌病害,会导致严重的种子产量损失,并降低其含油量和品质。本研究采用琼脂塞茎接种法,在四个环境中对 187 种不同的油菜基因型在盛花期的 SSR 反应进行了特征描述。利用三种不同的算法进行全基因组关联研究(GWAS),鉴定出与茎部病变长度(LL)、病变宽度(LW)和 14 天(PM_14D)和 21 天(PM_21D)时植株死亡率(PM_21D)等疾病性状相关的 133 个显著 SNP,对应 123 个位点。这些 SNP 解释的表型变异范围为 3.6%至 12.1%。在两个或更多环境中检测到 19 个显著 SNP,这些环境中至少有两种 GWAS 算法用于评估疾病性状。LL 与其他三种疾病性状之间观察到的强相关性表明,它们可以作为 SSR 抗性表型的替代物。鉴定出与疾病抗性机制相关的 69 个候选基因。利用全基因组标记对所有四个性状进行基因组预测(GP)分析,根据模型规范,预测能力在 0.41 到 0.64 之间。三种模型对 PM_21D 的最高预测能力约为 0.64。从我们的研究中,鉴定出的抗性基因型和稳定的显著 SNP 标记将成为未来 SSR 抗性育种的宝贵资源。我们的研究还表明,基因组选择通过减少对大量基因型进行表型分析的需求,使育种者能够在早期选择 SSR 抗性基因型,从而有望加速油菜育种进程。