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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 #378652

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

Location: Plant, Soil and Nutrition Research

Title: Utility of climatic information via combining ability models to improve genomic prediction for yield within the genomes to fields maize project

Author
item JARQUIN, DIEGO - University Of Nebraska
item DE LEON, NATALIA - University Of Wisconsin
item ROMAY, MARIA CINTA - Cornell University - New York
item BOHN, MARTIN - University Of Illinois
item Buckler, Edward - Ed
item CIAMPITTI, IGNACIO - Kansas State University
item Edwards, Jode
item ERTL, DAVID - Iowa Corn Promotion Board
item Flint-Garcia, Sherry
item Gore, Michael
item GRAHAM, CHRISTOPHER - South Dakota State University
item HIRSCH, CANDICE - University Of Minnesota
item Holland, Jim - Jim
item HOOKER, DAVE - University Of Guelph
item KAEPPLER, SHAWN - University Of Wisconsin
item Knoll, Joseph - Joe
item LEE, ELIZABETH - University Of Guelph
item LAWRENCE-DILL, CAROLYN - Iowa State University
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 SCHNABLE, JAMES - University Of Nebraska
item SCHNABLE, PATRICK - Iowa State University
item SMITH, MARGARET - Cornell University - New York
item SPRINGER, NATHAN - University Of Minnesota
item THOMISON, PETER - The Ohio State University
item TUINSTRA, MITCH - Purdue University
item WISSER, RANDALL - University Of Delaware
item XU, WENWEI - Texas A&M University
item LORENZ, AARON - University Of Minnesota

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/21/2020
Publication Date: 3/8/2021
Citation: Jarquin, D., De Leon, N., Romay, M., Bohn, M., Buckler IV, E.S., Ciampitti, I., Edwards, J.W., Ertl, D., Flint Garcia, S.A., Gore, M.A., Graham, C., Hirsch, C.N., Holland, J.B., Hooker, D., Kaeppler, S.M., Knoll, J.E., Lee, E.S., Lawrence-Dill, C.J., Lynch, J.P., Moose, S.P., Murray, S.C., Nelson, R., Rocheford, T., Schnable, J.C., Schnable, P.S., Smith, M., Springer, N., Thomison, P., Tuinstra, M., Wisser, R.J., Xu, W., Lorenz, A. 2021. Utility of climatic information via combining ability models to improve genomic prediction for yield within the genomes to fields maize project. Frontiers in Genetics. 11:592769. https://doi.org/10.3389/fgene.2020.592769.
DOI: https://doi.org/10.3389/fgene.2020.592769

Interpretive Summary: Using genomic prediction methods to breed desirable characteristics in plants is an efficient alternative to traditional phenotyping selection methods. Because collecting weather data is becoming more automated and is consequently measured continuously, weather data could theoretically be used to predict plant development as part of other genomic prediction methods. This study measured the usefulness of including weather data variables in genomic prediction methods. Results showed that including real-time, weather data variables did not improve genomic prediction over the traditional method of using models to predict the effects of weather. However, more research is warranted on the link between observed weather and predicting its effects on plant development.

Technical Abstract: Genomic prediction methods provide an efficient alternative to conventional phenotyping selection for developing improved cultivars with desirable characteristics. New and improved methods are continually developed. Some of these attempts deal with the integration of different data types beyond genomic and/or pedigree information. Modern and automated systems offer the opportunity to capture weather data, practically, in a continuous manner over a time. In principle, this information could better characterize training and target environments to enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (GxE) interaction component in the prediction models. Using a naive environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015, we assessed the usefulness of including weather data variables in genomic prediction models in four different prediction scenarios that included predicting i) tested genotypes in observed environments; ii) untested genotypes in observed environments; iii) tested genotypes in unobserved environments; and iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effects of environments (E), inbred markers for paternal and maternal effects GC (GP1, GP2); and the interactions between paternal and maternal marker effects SCA (GP1× P2 = GP1 × GP2), between inbred markers and environments (GP1 × E, GP2 × E and GP1 × P2 × E) and between the inbred markers and the Environmental Covariates ECs (GP1 × W, GP2 × W and GP1 × P2 × W). Results showed that the environmental kinship model did not improve predictive ability over prediction models that simply modeled the GxE interaction term. These results suggest that weather data per se is not enough for improving predictive ability. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.