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.

预测复杂的表型如何从遗传变异和发育环境的相互作用(即，基因与环境的相互作用)中出现已经在模型系统中变得越来越可行。做出这种预测的能力在许多领域都很重要，例如：了解疾病风险，增加减轻环境变化影响的潜力，以及提高先进农业品种的育种效率。然而，准确的预测并不意味着我们已经在个人或群体层面上实现了机械性的理解。解决这一机械性理解的挑战需要跨环境复制多种基因类型的能力，这一困难阻碍了对人类和脊椎动物模型系统中复杂特征的研究。作物是分析基因与环境相互作用机制所需的理想数据，也是数量遗传学的原始模型系统。这项研究为利用玉米田间试验开发和测试研究基因与环境互作的方法提供了机会，在玉米田间试验中，这种复制是高度可行的。为了从这些数据中提取对路径的机械性理解，该项目将开发对广泛物种有用的统计方法和软件。我们的研究中涉及的结果、方法和概念也可以通过促进共享的学习经验来扩展，帮助传统上没有代表性的学生挑战障碍，并与在基础科学和农业实际应用方面具有经验的研究人员建立合作关系。这个项目的主要目标是提供机械理解基因如何与可变环境相互作用来产生复杂的表型，特别是根系结构和基因在根节组织中的表达。一种基因对不同环境条件产生不同表型的现象被称为表型可塑性，是生物学中普遍存在的一个方面。这个项目将使用玉米根系结构性状作为一个模型系统，用于从机制上理解复杂性状的遗传学。这项研究将改进和验证在农业和生态相关的、充分浇水和受控干旱条件下的植物根系结构的田间高通量表型(HTP)。将在整个生命周期中收集这些HTP根的表型，以了解生长轨迹中潜在的多态现象。根组织的基因表达分析将使eQTL定位成为可能，并测试顺式和反式多态性对核心基因表达的相对作用。将对玉米双亲重组自交系群体进行表型鉴定。在这些群体中，将表型与因果多态联系起来，将涉及开发新的计算工具，以从对x基因环境(GxE)相互作用和时间序列数据的经验研究中提取最大数量的信息。总之，这些新的数据和分析方法将被用来识别基因x环境互作(GxE)和基因x时间互作(GXT)的多态。候选基因的功能作用和GxE的简单、多基因模型的实用性将在玉米和拟南芥中使用单一和更高级别的突变体进行测试。该项目有四个具体目标：1)在田间试验中对玉米根系中的QTL x E x时间进行实证研究；2)对根组织进行全基因组转录丰度分析，以便进行eQTL定位和测试顺式效应对核心基因表达的影响的预测；3)使用突变体、CRISPR编辑和大效应大小的GxE QTL的过表达来测试双子叶和单子叶植物对土壤水分的可塑性模型，4)开发基于多元线性模型的QTL作图方法来处理QTL x E x时间数据。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。