This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2020. 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 Dr. Penelope Lindsay is “Developing a maize meristem model to assess the impact of CLAVATA signaling on yield traits". The host institution for the fellowship is Cold Spring Harbor Laboratory and the sponsoring scientist is Dr. David Jackson.
The U.S. is the world’s largest producer of maize, producing 384 million tons annually. With a changing environment and burgeoning global population, we need to maximize maize yield while minimizing agricultural land use. One way to increase maize yield per plant is to increase the amount of seed produced per ear. The maize ear arises from a group of undifferentiated stem cells called the apical meristem. Subtle genetic changes between maize lines affect meristem growth and can have big impacts on yield. This project will build a computational framework to understand how genes function together to regulate the meristem and to form the ear and seeds. Maize lines are very diverse, and this will be used to our advantage to find how differences in these gene networks contribute to making the maize ear. This project will provide the fellow with training in maize genetics and genomics, coupled with bioinformatics and mathematical modeling techniques gleaned through collaborative research. Outreach in plant genetics will occur through High School student and teacher training in plant biology at GenSpace, a community lab space in Brooklyn, NY.
This project aims to understand the complex regulation of maize ear development through analysis of the nested association mapping (NAM) founders, a diverse panel of maize lines which vary in both meristem and inflorescence size. Precise phenotypic and transcriptomic analysis of these lines will inform the development of a maize inflorescence meristem model. The model will be used to modulate spatial and quantitative gene expression in silico to generate predictions for effects on maize yield. These predictions can be applied in precision breeding to maximize yield per plant, reducing land use and minimizing agricultural environmental impact. Data generated from this project will be shared in peer-reviewed publications and be presented at national scientific conferences. In addition, transcriptomic data generated from this project will be uploaded to the NCBI Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/). Code generated from modeling the genetic control of the maize inflorescence meristem will be shared on GitHub (https://github.com/).
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.
