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Minimize RSR Award Detail

Research Spending & Results

Award Detail

  • Cedar D Warman
Award Date:05/11/2021
Estimated Total Award Amount: $ 216,000
Funds Obligated to Date: $ 216,000
  • FY 2021=$216,000
Start Date:07/01/2021
End Date:06/30/2024
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.074
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:NSF Postdoctoral Fellowship in Biology FY 2021: Defining the Genetic Basis of Tomato Reproductive Heat Tolerance through Phenotyping, Genome-wide Association, & Predictive Modeling
Federal Award ID Number:2109832
Program:NPGI PostDoc Rsrch Fellowship
Program Officer:
  • Diane Jofuku Okamuro
  • (703) 292-4508

Awardee Location

Awardee Cong. District:

Primary Place of Performance

Organization Name:University of Arizona
Cong. District:03

Abstract at Time of Award

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 prediction 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.

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