EDGE CMT: Genetic basis of plant root growth traits and their response to environment

EDGE CMT：植物根部生长性状的遗传基础及其对环境的响应

• 批准号: 2220726
• 负责人: John McKay
• 金额: $ 200万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220726&HistoricalAwards=false
• 关键词: EDGE; CMT; Genetic; basis; plant

Predicting how complex phenotypes emerge from the interaction of genetic variation and developmental environment (i.e., genotype by environment interaction) has become increasingly feasible in model systems. The ability to make this kind of prediction is important in many fields, for example: understanding of disease risk, increase the potential for mitigating the effects of a changing environment, and improve the efficiency of breeding for advanced agricultural varieties. However, accurate prediction does not mean we have achieved mechanistic understanding at the individual or population level. Addressing this challenge of a mechanistic understanding requires the ability to replicate many genotypes across environments, a difficulty that has hindered studies of complex traits in humans and vertebrate model systems. Crops are ideal for generating the data needed for dissecting mechanisms of genotype by environment interactions and are the original model systems for quantitative genetics. This research provides an opportunity to develop and test methods for studying genotype by environment interactions using maize field trials, a system where such replication is highly feasible. To extract mechanistic understanding of pathways from these data, this project will develop statistical methods and software that will be useful for a broad range of species. The results, methods, and concepts addressed in our research could also be extended by facilitating a shared learning experience that help traditionally unrepresented students challenge barriers and build collaborative relationships with researchers with experience in bridging fundamental science with pragmatic application in agriculture.This main goal of this project is to provide mechanistic understanding how genes interact with variable environments to produce complex phenotypes, specifically root system architecture and gene expression in nodal root tissues. The phenomenon of a genotype producing different phenotypes in response to different environmental conditions is termed phenotypic plasticity and is a ubiquitous aspect of biology. This project will use maize root system architecture traits as a model system for mechanistic understanding of the genetics of complex traits. The research will refine and validate field-based high-throughput phenotyping (HTP) of plant root system architecture under agriculturally and ecologically relevant well-watered and controlled-drought conditions. These HTP root phenotypes will be collected across the lifecycle to understand the polymorphisms underlying differences in growth trajectories. Gene expression analysis of root tissue will allow eQTL mapping and test the relative role of cis and trans polymorphisms on expression of core genes. Phenotyping will be performed on bi-parental recombinant inbred populations of maize. Linking phenotypes to causal polymorphisms in these populations will involve the development of new computational tools for extracting the maximum amount of information from empirical studies of Genotype x Environment (GxE) interactions and time-series data. Together, these novel data and analysis methods will be used to identify polymorphisms underlying genotype x environment interactions (GxE) and genotype x time interactions (GxT). The functional roles of candidate genes and the utility of simple, multi-gene models of GxE will be tested using single and higher order mutants in maize and Arabidopsis thaliana. This project has four specific aims : 1)Perform empirical studies of QTL x E x Time in maize roots in field studies, 2) Perform whole genome transcript abundance analysis of root tissue to allow eQTL mapping and test predictions of the role of cis effects on core gene expression, 3) Use mutants, CRISPR edits and overexpression of large effect size GxE QTL to test models of plasticity to soil moisture in both dicot and monocot species, 4) Develop QTL mapping methods based on multivariate linear models to handle QTL x E x Time data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在模型系统中，预测遗传变异与发育环境的相互作用（即基因型）如何从模型系统中变得越来越可行。例如，在许多领域中进行这种预测的能力很重要，例如：了解疾病风险，增加减轻环境不断变化的影响的潜力，并提高繁殖效率对先进的农业品种的效率。但是，准确的预测并不意味着我们已经在个人或人口水平上实现了机械理解。应对机械理解的这一挑战需要能够在环境中复制许多基因型的能力，这一困难阻碍了人们对人类和脊椎动物模型系统中复杂性状的研究。农作物是通过环境相互作用来解剖基因型所需的数据的理想选择，并且是定量遗传学的原始模型系统。这项研究提供了一个机会，可以使用玉米现场试验来开发和测试通过环境相互作用来研究基因型的机会，玉米现场试验是高度可行的系统。为了从这些数据中提取对途径的机械理解，该项目将开发统计方法和软件，这些方法和软件将对广泛的物种有用。 The results, methods, and concepts addressed in our research could also be extended by facilitating a shared learning experience that help traditionally unrepresented students challenge barriers and build collaborative relationships with researchers with experience in bridging fundamental science with pragmatic application in agriculture.This main goal of this project is to provide mechanistic understanding how genes interact with variable environments to produce complex phenotypes, specifically root system architecture and gene expression in nodal root组织。对不同环境条件产生不同表型的基因型的现象称为表型可塑性，是生物学无处不在的方面。该项目将使用玉米根系架构特征作为模型系统，以机械理解复杂性状的遗传学。这项研究将在农业和生态相关的含水良好和受控的干旱条件下完善并验证植物根系结构的植物根系结构的基于现场的高通量表型（HTP）。这些HTP根表型将在整个生命周期中收集，以了解生长轨迹差异的基本多态性。根组织的基因表达分析将允许EQTL映射并测试顺式和反式多态性对核心基因表达的相对作用。表型将对玉米的双生重组人群进行。将表型与这些人群的因果多态性联系起来将涉及开发新的计算工具，以从基因型X环境（GXE）相互作用和时间序列数据中提取最大信息量。总之，这些新型数据和分析方法将用于识别基因型X环境相互作用（GXE）和基因型X时间相互作用（GXT）的多态性。 候选基因的功能作用以及简单的，多基因GXE模型的实用性将使用玉米和拟南芥中的单一和高级突变体进行测试。 This project has four specific aims : 1)Perform empirical studies of QTL x E x Time in maize roots in field studies, 2) Perform whole genome transcript abundance analysis of root tissue to allow eQTL mapping and test predictions of the role of cis effects on core gene expression, 3) Use mutants, CRISPR edits and overexpression of large effect size GxE QTL to test models of plasticity to soil moisture in both dicot and monocot species, 4）开发基于多元线性模型来处理QTL X E X时间数据的QTL映射方法。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。