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

本行动资助2021财年美国国家科学基金会植物基因组生物学博士后研究奖学金。该奖学金支持奖学金获得者在主办实验室的研究和培训计划，该奖学金获得者还提出了扩大生物学参与的计划。这项奖学金的研究和培训计划的标题是“通过高通量花粉管表型、全基因组关联和预测建模来定义番茄生殖耐热性的遗传基础”。该奖学金的主办机构是亚利桑那大学，赞助科学家是Ravishankar Palanivelu博士。植物繁殖对热胁迫高度敏感。植物繁殖失败可能导致作物产量损失，随着气候变化中温度的升高，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发现番茄基因组中与耐热性和易感性相关的可变区域。这些区域的鉴定将有助于对约1000个番茄品种的耐热性进行预测。该项目将回答有关植物抵抗热应激机制的基本问题；它还将为测量植物对环境的反应提供新的方法，并为根据这些测量结果进行预测提供新的策略。在这个项目的过程中，研究员将接受一个跨学科的科学家指导小组在遗传学和计算生物学方面的培训。这个项目将被用作一个案例研究，以创建一个互动课程，在当地高中展示，其中包括大多数传统上在美国科学领域代表性不足的学生。植物繁殖的关键步骤，包括花粉的发育和功能，即使在短时间的过热后也会中断，导致不完全受精和种子和果实产量下降。本项目将利用一种新型的高通量成像系统，对200个番茄品种及其野生近缘种的花粉管在热胁迫下的生长表型进行研究。与这些表型变异相关的遗传位点将使用全基因组关联研究（GWAS）进行鉴定。这些数据将构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，并对这些预测的子集进行功能验证，以评估模型的准确性。在这个项目过程中开发的表型、位点鉴定和基因组预测管道将很容易适用于其他作物。该项目产生的所有数据将向公众发布，包括用于高通量表型的深度学习模型和训练数据集。更广泛地了解花粉管生长过程中的热胁迫，将指导未来在番茄和其他重要农业物种中创造新的耐热品种的育种工作。关键词：番茄，环境胁迫，热胁迫，耐热性，遗传学，表型，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，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.