Developing a New Paradigm for Quantitative Genetic Modeling: Integrating Molecular, Physiological, and Environmental Variation

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This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2017. 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 host institutions for the fellowship are Cornell University and the University of Queensland, and the sponsoring scientists are Dr. Edward S. Buckler and Dr. Graeme Hammer.Modern technologies and computational approaches have led to predictive crop models with the potential to shorten breeding cycles and increase the efficiency of crop improvement. However, these tools are in their infancy and usually underperform in actual field conditions where variable environmental conditions exist. This project will incorporate available forms of biological and environmental data into an improved modeling framework to more accurately predict plant growth and to enable more efficient crop production. To assist in this method development, the Fellow will receive training and mentoring in statistical, computational, and mathematical modeling from two of the world's foremost experts in these areas. The Fellow will also develop and conduct training workshops to assist others in using the newly developed methods.Quantitative genetic models are critical to understanding how molecular and environmental factors interact and contribute to phenotypes. They provide a mathematical and theoretical paradigm for research on, and prediction of, complex and economically important traits in agriculture, conservation and medicine. However, they often perform poorly when predicting complex or emergent properties, hybrid progeny phenotypes, and traits strongly influenced by the environment. The current paradigm also ignores a great deal of biological knowledge from genomics, transcriptomics, physiology, evolutionary biology, and elsewhere. This project will develop new quantitative genetic methods that better incorporate biological data from genomics, transcriptomics, physiology, and dosage models. All data and methods generated through the project will be made publically available in peer reviewed journal articles, national repositories, and open source software version control platforms.