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

本行动资助2017财年美国国家科学基金会国家植物基因组计划生物学博士后研究奖学金。该奖学金支持奖学金获得者在主办实验室的研究和培训计划，该奖学金获得者还提出了扩大生物学参与的计划。该奖学金的主办机构是康奈尔大学和昆士兰大学，赞助科学家是爱德华·s·巴克尔博士和格雷姆·哈默博士。现代技术和计算方法已经产生了预测作物模型，具有缩短育种周期和提高作物改良效率的潜力。然而，这些工具还处于起步阶段，在多变的实际现场条件下通常表现不佳。该项目将把现有形式的生物和环境数据纳入改进的建模框架，以更准确地预测植物生长并提高作物生产效率。为了协助这一方法的发展，研究员将从这些领域的两位世界上最重要的专家那里接受统计、计算和数学建模方面的培训和指导。该研究员还将发展和举办培训讲习班，以协助其他人使用新开发的方法。定量遗传模型对于理解分子和环境因素如何相互作用并促成表型至关重要。它们为研究和预测农业、保护和医学中复杂和经济上重要的性状提供了数学和理论范式。然而，在预测复杂或突现特性、杂交后代表型和受环境强烈影响的性状时，它们往往表现不佳。目前的范式还忽略了基因组学、转录组学、生理学、进化生物学和其他领域的大量生物学知识。该项目将开发新的定量遗传方法，更好地结合基因组学、转录组学、生理学和剂量模型的生物学数据。通过该项目产生的所有数据和方法将在同行评审的期刊文章、国家存储库和开源软件版本控制平台上公开提供。

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.