EAGER: Predicting Drought Adaptation in C4 Plants with High Throughout Quantitative Phenotyping

EAGER：通过高通量定量表型预测 C4 植物的干旱适应

• 批准号: 1450341
• 负责人: Brian Dilkes
• 金额: $ 29.96万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1450341&HistoricalAwards=false
• 关键词: EAGER; Predicting; Drought; Adaptation; C4

This EAGER project will test whether use of a novel combination of high-throughput field-scale measurement of ion accumulation levels and carbon isotope discrimination can predict yield under stress in maize. If successful, this project will generate a new paradigm for the study of abiotic stress tolerance in maize and other C4 plants and the immediate improvement of crop abiotic stress tolerance by the plant breeding industry. It could also enable scientists to rapidly predict the consequence of genotype on adaptation to increasing temperature, decreasing water arability, and other predicted outcomes of global climate change. A postdoctoral scientist will receive training and mentorship in the integration of high throughput phenotyping, bioinformatics and quantitative genetics. All data generated in this study will be accessible through iHUB (www.ionomicshub.org) and the Purdue University Research Repository (http://purr.purdue.edu). Many phenotypes used for drought tolerance estimation in plants are slow, costly, and of insufficient heritability to make substantial progress connecting genotype to phenotype. Measures of water relations often assess dynamic responses of the plant to the local environment, are strongly influenced by temporal factors and unusable for genetics. The absence of high throughput quantitative phenotyping approaches (e.g. phenomics) that precisely report the integrative physiological status of field-grown plants is hampering the discovery of molecular mechanisms of plant adaptation. This problem is particularly notable in plants with C4 photosynthesis and has limited the discovery of the mechanisms controlling water use in these drought adaptable species and inhibited crop improvement in maize and in many target biofuel crops including sorghum and switchgrass. Preliminary data suggests a correlation between delta-13C, the ratio of stable isotopes 13C:12C, and yield under drought and the relationship between delta-13C, water relations, and photosynthetic capacity in C4 plants. This EAGER project will test the hypothesis that the combination of water-soluble element accumulation and carbon isotope ratios in mature maize kernels can predict adaptation to drought. The specific objectives include a genome-wide assessment of genetic contribution to these traits by genome-wide association study. Both traits will be assessed within an association panel of maize in the field under a managed drought environment.

这个EAGER项目将测试是否使用一种新的组合，高通量的离子积累水平和碳同位素的歧视，现场规模的测量可以预测玉米在压力下的产量。如果成功，该项目将为玉米和其他C4植物的非生物胁迫耐受性研究以及植物育种业对作物非生物胁迫耐受性的直接改善提供新的范例。它还可以使科学家们能够快速预测基因型对适应温度升高、水资源利用率下降以及全球气候变化的其他预测结果的影响。博士后科学家将接受高通量表型分析，生物信息学和定量遗传学整合的培训和指导。 本研究生成的所有数据均可通过iHUB（www.ionomicshub.org）和Purdue University Research Repository（http：purr.purdue.edu）访问。许多用于植物耐旱性评估的表型是缓慢的，昂贵的，并且遗传力不足，无法在基因型与表型之间取得实质性进展。水关系的措施往往评估植物对当地环境的动态响应，受时间因素的影响很大，不能用于遗传学。缺乏高通量的定量表型分析方法（如表型组学），精确地报告田间生长的植物的综合生理状态，阻碍了植物适应的分子机制的发现。这一问题在具有C4光合作用的植物中尤其显著，并且限制了在这些干旱适应性物种中控制水利用的机制的发现，并且抑制了玉米和许多目标生物燃料作物（包括高粱和柳枝稷）中的作物改良。初步数据表明，三角洲-13 C之间的相关性，稳定同位素13 C：12 C的比例，在干旱和三角洲-13 C之间的关系，水的关系，光合能力在C4植物和产量。这个EAGER项目将检验这样一个假设，即成熟玉米籽粒中水溶性元素积累和碳同位素比率的结合可以预测对干旱的适应。具体目标包括通过全基因组关联研究对这些性状的遗传贡献进行全基因组评估。这两个性状将在管理干旱环境下田间玉米的关联小组内进行评估。