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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #354080

Research Project: Improving Crop Efficiency Using Genomic Diversity and Computational Modeling

Location: Plant, Soil and Nutrition Research

Title: Maize genomes to fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Author
item Alkhalifah, Naser - University Of Wisconsin
item Campbell, Darwin - Iowa State University
item Falcon, Celeste - University Of Wisconsin
item Miller, Nathan - University Of Wisconsin
item Romay, Maria Cinta - Cornell University - New York
item Walls, Ramona - Cornell University - New York
item Walton, Renee - Iowa State University
item Yeh, Cheng-ting - Iowa State University
item Bohn, Martin - University Of Illinois
item Buckler, Edward - Ed
item Ciampitti, Ignacio - Kansas State University
item Flint-garcia, Sherry
item Gore, Michael - Cornell University - New York
item Graham, Christopher - South Dakota State University
item Hirsch, Candice - University Of Minnesota
item Holland, Jim - Jim
item Hooker, David - University Of Guelph
item Kaeppler, Shawn - University Of Wisconsin
item Knoll, Joseph - Joe
item Lauter, Nicholas
item Lee, Elizabeth - University Of Guelph
item Lorenz, Aaron - University Of Minnesota
item Lynch, Jonathan - Pennsylvania State University
item Moose, Stephen - University Of Illinois
item Murray, Seth - Texas A&M University
item Nelson, Rebecca - Cornell University - New York
item Rocheford, Torbert - Purdue University
item Rodriguez, Oscar - University Of Nebraska
item Schnable, James - University Of Nebraska
item Scully, Brian
item Smith, Margaret - Cornell University - New York
item Springer, Nathan - University Of Minnesota
item Thomison, Peter - The Ohio State University
item Tuinstra, Mitchell - Purdue University
item Wisser, Randy - University Of Delaware
item Xu, Wenwei - Texas A&M University
item Ertl, David - Iowa Corn Promotion Board
item Schnable, Patrick - Iowa State University
item De Leon, Natalia - University Of Wisconsin
item Spalding, Edgar - University Of Wisconsin
item Edwards, Jode
item Lawrence-dill, Carolyn - Iowa State University

Submitted to: Biomed Central (BMC) Plant Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/18/2018
Publication Date: 7/9/2018
Citation: Alkhalifah, N., Campbell, D., Falcon, C., Miller, N., Romay, M., Walls, R., Walton, R., Yeh, C., Bohn, M., Buckler IV, E.S., Ciampitti, I., Flint Garcia, S.A., Gore, M., Graham, C., Hirsch, C., Holland, J.B., Hooker, D., Kaeppler, S., Knoll, J.E., Lauter, N.C., Lee, E., Lorenz, A., Lynch, J., Moose, S., Murray, S., Nelson, R., Rocheford, T., Rodriguez, O., Schnable, J., Scully, B.T., Smith, M., Springer, N., Thomison, P., Tuinstra, M., Wisser, R., Xu, W., Ertl, D., Schnable, P., De Leon, N., Spalding, E., Edwards, J.W., Lawrence-Dill, C. 2018. Maize genomes to fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets. Biomed Central (BMC) Plant Biology. 11:452. https://doi.org/10.1186/s13104-018-3508-1.
DOI: https://doi.org/10.1186/s13104-018-3508-1

Interpretive Summary: Development of new crop varieties is a data driven process that relies on integration of multiple sources of data. Integration of genetic data, performance data, and environmental data for the environments in which performance was measured provides rich source of information from which scientists can ask many questions about variety performance and response to differing environments. The Genomes to Fields project as assembled a large data set encompassing several hundred maize hybrids grown across dozens of environments. The project is an ongoing project that will continue to accumulate new data as a resource for diverse scientific inquiry.

Technical Abstract: Developing improved crops relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets diverse queries can be made: Which lines perform best in hot, dry environments versus wet environments? Which genes and alleles of specific genes are required for optimal performance in each? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional umbrella organization of scientists working to generate and analyze such datasets. G2F’s Genotype by Environment (GxE) project has made public releases of 2014 and 2015 datasets with 2016 and 2017 collected and soon to be made available as well.