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

开发定量遗传模型的新范式：整合分子、生理和环境变异

• 批准号: 1710618
• 负责人: Jacob Washburn
• 金额: $ 21.6万
• 依托单位: Washburn Jacob D
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1710618&HistoricalAwards=false
• 关键词: Developing; New; Paradigm; Quantitative; Genetic

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.

该行动资助了2017财年NSF国家植物基因组计划生物学博士后研究奖学金。该研究金支持研究员在东道实验室的研究和培训计划，研究员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州大学，赞助科学家是爱德华·S·Buckler和Graeme Hammer博士。现代技术和计算方法已经导致预测作物模型，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，并且在存在可变环境条件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，并实现更有效的作物生产。为了协助这种方法的发展，研究员将接受来自世界上最重要的专家在这些领域的统计，计算和数学建模的培训和指导。研究员还将开发和举办培训讲习班，以协助其他人使用新开发的方法。定量遗传模型对于了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，他们往往表现不佳时，预测复杂的或新兴的性质，杂交后代表型，并强烈受环境影响的性状。当前的范式还忽视了来自基因组学、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，更好地结合来自基因组学，转录组学，生理学和剂量模型的生物数据。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、国家知识库和开源软件版本控制平台中公开提供。

项目成果

Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models

• DOI: 10.1002/csc2.20052
• 发表时间: 2020-03-01
• 期刊: CROP SCIENCE
• 影响因子: 2.3
• 作者: Washburn, Jacob D.;Burch, Merritt B.;Franco, Jose A. Valdes
• 通讯作者: Franco, Jose A. Valdes

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence

• DOI: 10.1073/pnas.1814551116
• 发表时间: 2019-03-19
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Washburn, Jacob D.;Mejia-Guerra, Maria Katherine;Wang, Hai
• 通讯作者: Wang, Hai