Tapia Ronald, Abd-Elrahman Amr, Osorio Luis, Whitaker Vance M, Lee Seonghee
Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA.
Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA.
J Exp Bot. 2022 Sep 3;73(15):5322-5335. doi: 10.1093/jxb/erac136.
High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet-visible-near infrared region (350-1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics-genomics approach in strawberry breeding.
高通量表型分析是植物科学中一种新兴的方法,但迄今为止,在园艺作物育种中的应用还很少。对叶片或冠层光谱反射率进行遥感有助于育种者快速测量性状、提高选择准确性,从而改善对选择的响应。在本研究中,我们评估了冠层反射光谱分析与基因组信息相结合用于预测草莓(Fragaria × ananassa)白粉病的效果。在两个不同季节对两个多亲本草莓育种群体进行了评估,这两个群体共有340株和464株有谱系关联的实生苗。使用紫外 - 可见 - 近红外区域(波长350 - 1350 nm)的1001个光谱波段结合8552个单核苷酸多态性标记的单性状贝叶斯预测方法,其预测能力比仅使用标记的模型提高了两倍。高通量表型分析的整合在多年/多次试验中得到了进一步独立验证,对选择的响应提高了90%。我们还使用估计的植被指数作为次要性状进行了贝叶斯多性状分析。三个植被指数(Datt3、REP_Li和Vogelmann2)与白粉病视觉评级具有较高的遗传相关性(rA),平均rA值分别为0.76、0.71和0.71。通过纳入仅具有植被指数信息的个体来增加训练群体规模,可显著提高预测能力。这些结果有力地表明可将植被指数用作间接选择的次要性状。总体而言,结合光谱分析和全基因组预测提高了对白粉病抗性的选择准确性和对选择的响应,证明了综合表型组学 - 基因组学方法在草莓育种中的强大作用。