Abstract：
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.
摘要：
高通量表型鉴定是植物科学中的一种新兴方法，但是到目前为止，在园艺作物育种中只有很少的应用。叶片或冠层光谱反射率的遥感可以帮助育种者快速测量性状，提高选择的准确性，从而提高对选择的反应。在本研究中,我们评估了冠层反射率的光谱分析和基因组信息的集成，以预测草莓（Fragaria×ananassa）白粉病。在两个不同的季节中评估了两个多亲本草莓育种种群，总共包括340和464个谱系连接的幼苗。使用紫外-可见-近红外区域（350-1350nm波长）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显示，预测能力比单独使用标记的模型提高了2倍。高通量表型的整合在多年/试验中得到了独立的进一步验证，对选择的反应提高了90%。我们还使用估计的营养指数作为次要性状进行了贝叶斯多性状分析。三个植物指数(Datt3，REP_Li，和Vogelmann2）与白粉病视觉等级具有很高的遗传相关性（rA），平均rA值分别为0.76、0.71和0.71。通过仅将植物指数信息纳入个体来增加训练人口规模，从而大大提高了预测能力。这些结果强烈表明，将营养指数用作间接选择的次要性状。总的来说,光谱法和全基因组预测相结合，提高了抗白粉病选择的准确性和响应，展示了整合的表型-基因组学方法在草莓育种中的力量。
