Yang Wanneng, Guo Zilong, Huang Chenglong, Wang Ke, Jiang Ni, Feng Hui, Chen Guoxing, Liu Qian, Xiong Lizhong
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, PR China College of Engineering, Huazhong Agricultural University, Wuhan 430070, PR China Agricultural Bioinformatics Key Laboratory of Hubei Province, Huazhong Agricultural University, Wuhan 430070, PR China.
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, PR China.
J Exp Bot. 2015 Sep;66(18):5605-15. doi: 10.1093/jxb/erv100. Epub 2015 Mar 20.
Leaves are the plant's solar panel and food factory, and leaf traits are always key issues to investigate in plant research. Traditional methods for leaf trait measurement are time-consuming. In this work, an engineering prototype has been established for high-throughput leaf scoring (HLS) of a large number of Oryza sativa accessions. The mean absolute per cent of errors in traditional measurements versus HLS were below 5% for leaf number, area, shape, and colour. Moreover, HLS can measure up to 30 leaves per minute. To demonstrate the usefulness of HLS in dissecting the genetic bases of leaf traits, a genome-wide association study (GWAS) was performed for 29 leaf traits related to leaf size, shape, and colour at three growth stages using HLS on a panel of 533 rice accessions. Nine associated loci contained known leaf-related genes, such as Nal1 for controlling the leaf width. In addition, a total of 73, 123, and 177 new loci were detected for traits associated with leaf size, colour, and shape, respectively. In summary, after evaluating the performance with a large number of rice accessions, the combination of GWAS and high-throughput leaf phenotyping (HLS) has proven a valuable strategy to identify the genetic loci controlling rice leaf traits.
叶片是植物的太阳能板和食物工厂，叶片性状一直是植物研究中的关键研究对象。传统的叶片性状测量方法耗时较长。在这项研究中，已经建立了一个工程原型，用于对大量水稻品种进行高通量叶片评分（HLS）。传统测量与HLS在叶片数量、面积、形状和颜色方面的平均绝对误差百分比低于5%。此外，HLS每分钟最多可测量30片叶子。为了证明HLS在剖析叶片性状遗传基础方面的实用性，利用HLS对533份水稻品种组成的群体在三个生长阶段的29个与叶片大小、形状和颜色相关的叶片性状进行了全基因组关联研究（GWAS）。九个关联位点包含已知的与叶片相关的基因，如控制叶片宽度的Nal1。此外，分别检测到与叶片大小、颜色和形状相关性状的新位点73个、123个和177个。总之，在对大量水稻品种进行性能评估后，GWAS与高通量叶片表型分析（HLS）的结合已被证明是鉴定控制水稻叶片性状遗传位点的一种有价值的策略。
