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

预测复杂表型如何从遗传变异和发育环境的相互作用中产生（即环境相互作用的基因型）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体或群体水平上获得了机制上的理解。解决这一机制理解的挑战需要在不同环境中复制许多基因型的能力，这一困难阻碍了对人类和脊椎动物模型系统中复杂性状的研究。作物是产生通过环境相互作用剖析基因型机制所需数据的理想材料，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通过环境相互作用研究基因型的方法提供了一个机会，在这个系统中，这种复制是高度可行的。为了从这些数据中提取对途径的机制理解，该项目将开发统计方法和软件，这将对广泛的物种有用。我们研究的结果、方法和概念也可以通过促进共享学习经验来扩展，帮助传统上没有代表性的学生挑战障碍，并与具有将基础科学与农业实际应用相结合经验的研究人员建立合作关系。该项目的主要目标是提供基因如何与可变环境相互作用以产生复杂表型的机制理解，特别是根系结构和结根组织中的基因表达。基因型在不同环境条件下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。本项目将利用玉米根系结构性状作为复杂性状遗传机制理解的模型系统。该研究将完善和验证在农业和生态相关的丰水和干旱控制条件下植物根系结构的田间高通量表型（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背后的多态性。根组织的基因表达分析将允许进行eQTL定位，并测试顺式和反式多态性对核心基因表达的相对作用。将对玉米双亲本重组自交系群体进行表型分析。将这些群体的表型与因果多态性联系起来，将涉及开发新的计算工具，以便从基因型x环境（GxE）相互作用的实证研究和时间序列数据中提取最大数量的信息。总之，这些新的数据和分析方法将用于鉴定基因型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和简单的多基因GxE模型的效用将通过玉米和拟南芥的单阶和高阶突变体进行测试。该项目有四个具体目标：1)进行实证研究的QTL E x在玉米根现场研究,2)执行全基因组转录丰度分析的根组织允许eQTL映射和测试的预测cis影响核心基因表达的作用,3)使用突变体,CRISPR编辑和过度的大塑性的影响大小GxE QTL检测模型,土壤水分在双子叶植物和单子叶植物物种,4)开发基于多元线性模型的QTL定位方法来处理QTL E x的时间数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.