El-Hendawy Salah, Junaid Muhammad Bilawal, Al-Suhaibani Nasser, Al-Ashkar Ibrahim, Al-Doss Abdullah
Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia.
Plants (Basel). 2024 Sep 19;13(18):2610. doi: 10.3390/plants13182610.
Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences ( ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R = 0.56-0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes.
目前，小麦育种计划正专注于使用无损且经济高效的高光谱传感工具，以便快速、准确地对大量基因型进行表型分析。预计这种方法将加速育种计划中基因型对非生物胁迫耐受性的培育进程。本研究旨在利用无损冠层光谱反射率测量来评估小麦基因型的耐盐性，以此替代直接的、费力且耗时的物候选择标准。八个小麦基因型和十六个F代重组自交系（RILs）在150 mM氯化钠的实际田间条件下进行了为期两年的测试。从光谱数据中计算出了14个光谱反射率指数（SRIs），包括植被SRIs和水分SRIs。使用遗传参数、SSR标记、Mantel检验、层次聚类热图、逐步多元线性回归和主成分分析（PCA），将这些指数在评估耐盐性方面的有效性与四个形态生理性状进行了比较。结果表明，RILs/品种在性状和SRIs方面均存在显著差异（≤0.001）。大多数SRIs的遗传力、遗传增益以及基因型和表型变异系数与实测性状相当。SRIs能够有效区分耐盐和敏感基因型，并与SSR标记表现出强相关性（R = 0.56 - 0.89），类似于实测性状和34个SSR的等位基因数据。基于Mantel检验，发现SRIs与SSR数据的相似系数之间存在强相关性（r = 0.27，< 0.0001），高于实测性状与SSR数据之间的相关性（r = 0.20，< 0.0003）。PCA表明，所有植被SRIs和大多数水分SRIs与实测性状在正向方向上聚类，并有效识别出耐盐RILs/品种。与使用单个SRIs相比，基于所有SRIs的偏最小二乘回归（PLSR）模型能够准确且稳健地估计各种形态生理性状。该研究表明，各种SRIs可与PLSR相结合，在小麦育种计划中作为一种经济高效的无损工具，用于在短时间内对大量小麦群体进行表型分析和耐盐性筛选。这种方法可以取代对传统形态生理性状的需求，并加速耐盐小麦基因型的培育。
