NSF Postdoctoral Fellowship in Biology FY 2021: Defining the Genetic Basis of Tomato Reproductive Heat Tolerance through Phenotyping, Genome-wide Association, & Predictive Mode

该行动为2021财年的NSF植物基因组博士后生物学研究奖学金提供资金。该研究金支持研究员在东道实验室的研究和培训计划，研究员还提出了扩大生物学参与的计划。Cedar沃曼的研究和培训计划的标题是“通过高通量花粉管表型分析、全基因组关联和预测建模确定番茄生殖耐热性的遗传基础”。“该研究金的主办机构是亚利桑那大学，赞助科学家是Ravishankar Palanivelu博士。植物繁殖的失败可能导致作物产量损失，随着气候变化中气温的上升，这些损失的经济和社会影响可能会增加。一些植物品种比其他品种更能抵抗热胁迫：在这个项目中，将在繁殖过程中测量200个对热胁迫表现出广泛反应的番茄品种。这些测量结果将用于寻找番茄基因组中与热胁迫抗性和易感性相关的可变区域。这些区域的识别将使预测的耐热性约1000番茄品种。该项目将回答有关植物用于抵抗热胁迫的机制的基本问题;它还将为测量植物对环境的反应提供新方法，并为从这些测量中进行预测提供新策略。在这个项目的过程中，研究员将在遗传学和计算生物学方面接受跨学科指导科学家小组的培训。该项目将被用作一个案例研究，以创建一个互动课程，在当地高中与注册，其中包括谁一直在美国传统上代表性不足的学生的大部分。植物繁殖的关键步骤，包括花粉的发育和功能，即使在短暂的过热之后也会被破坏，导致受精不完全，种子和果实产量减少。该项目将使用一种新型的高通量成像系统调查200个番茄栽培品种和野生近缘种在热胁迫下的花粉管生长表型。将使用全基因组关联研究（GWAS）鉴定与这些表型变异相关的遗传基因座。这些数据将形成基因组预测模型的基础，该模型将用于预测约1000个测序番茄品种在热胁迫下的表型，这些预测的子集在功能上经过验证，以评估模型的准确性。在该项目过程中开发的表型分析、基因座鉴定和基因组预测管道将很容易适应其他作物。该项目生成的所有数据都将向公众发布，包括用于高通量表型分析的深度学习模型和训练数据集。对花粉管生长过程中热胁迫的更广泛的理解将指导未来的育种工作，以创造番茄和其他农业重要物种的新型耐热品种。 关键词：番茄，环境胁迫，热胁迫，耐热性，遗传学，表型分析，计算机视觉，深度学习，全基因组关联，基因组预测该奖项反映了NSF的法定使命，并被认为是值得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的支持。

This action funds an NSF Plant Genome Postdoctoral Research Fellowship in Biology for FY 2021. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Cedar Warman is " Defining the Genetic Basis of Reproductive Heat Tolerance in Tomato through High-throughput Pollen Tube Phenotyping, Genome-wide association, and Predictive Modeling." The host institution for the fellowship is the University of Arizona and the sponsoring scientist is Dr. Ravishankar Palanivelu.Plant reproduction is highly sensitive to heat stress. Failures in plant reproduction can lead to crop yield losses and the economic and social impacts of these losses are likely to increase as temperatures rise in a changing climate. Some plant varieties are more resistant to heat stress than others: in this project, 200 tomato varieties that show a wide range of responses to heat stress will be measured during reproduction. These measurements will then be used to find variable regions of the tomato genome that are associated with resistance and susceptibility to heat stress. The identification of these regions will enable predictions to be made for the heat tolerance of ~1000 tomato cultivars. This project will answer fundamental questions about the mechanisms plants use to resist heat stress; it will also contribute new methods for measuring plant responses to the environment and new strategies for making predictions from these measurements. Over the course of this project, the Fellow will be trained in genetics and computational biology by an interdisciplinary group of mentoring scientists. This project will be used as a case study to create an interactive curriculum to be presented at local high schools with enrollments that include a majority of students who have been traditionally underrepresented in U.S. science. Key steps of plant reproduction, including the development and function of pollen, are disrupted after even short periods of excess heat, leading to incomplete fertilization and a reduction in seed and fruit yield. This project will survey pollen tube growth phenotypes under heat stress from a diverse panel of 200 tomato cultivars and wild relatives using a novel high-throughput imaging system. Genetic loci associated with variation in these phenotypes will be identified using genome-wide association studies (GWAS). These data will form the basis of a genomic prediction model that will be used to predict phenotypes under heat stress for ~1000 sequenced tomato cultivars, with a subset of these predictions functionally validated to assess model accuracy. The phenotyping, loci identification, and genomic prediction pipelines developed over the course of this project will be readily adaptable to other crops. All data generated in this project will be released to the public, including deep-learning models and training datasets used for high-throughput phenotyping. A broader understanding of heat stress during pollen tube growth will guide future breeding efforts to create novel heat tolerant varieties both in tomato and in other agriculturally important species. Keywords: tomato, environmental stress, heat stress, thermotolerance, genetics, phenotyping, computer vision, deep learning, genome-wide association, genomic predictionThis 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.