TRTech-PGR: Maize Hub: An Open-source Simulation Platform & Resources Integrating Extensive Field Trial Data, DNA Sequences, and Historical Weather Records

TRTech-PGR：Maize Hub：开源仿真平台

• 批准号: 2035472
• 负责人: Gustavo de los Campos
• 金额: $ 150万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035472&HistoricalAwards=false
• 关键词: TRTech; PGR; Maize; Hub; Open

Genetic-by-environment (G×E) interactions are a major source of variation in plant phenotypes. This makes breeding for target environments exceptionally challenging because future environmental conditions are mainly uncertain. To achieve fast genetic gains, breeding programs need to advance genotypes with limited years of testing. Thus, in the early stages of breeding, many cultivars are selected without testing them under weather conditions (e.g., drought, cold, or heat stress) that may critically affect their performance. To address the longstanding problem of predicting cultivars' phenotypes under largely uncertain weather conditions, this project will develop a computer simulation platform that will integrate field trial data, DNA sequences, and historical weather records into models that will enable researchers and breeders to predict plant phenotypes under likely weather conditions. An interdisciplinary team of data scientists, maize geneticists and breeders will use reaction-norm models and Deep Learning, a methodology that has proven to be very effective at capturing complex patterns in high-dimensional data, to learn G×E patterns from DNA sequences, environmental covariates, and phenotype data from the Genomes to Fields (G2F) project, the largest publicly initiated and led G×E research initiative. The models will be used together with historical weather data, to simulate and predict maize performances for a diverse set of maize hybrids at many locations within the U.S. The simulated data will then be used to uncover genetic regions underlying G×E, with findings validated using data generated in trials with controlled weather conditions. With respect to broader impacts, the project will provide interdisciplinary training in data science and genomics to students and postdoctoral fellows and will leverage existing programs to provide outreach for high school students and the general public. All project outcomes will be accessible through a dedicated project database and publicly available long-term repositories. All software will be released as open-source.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

遗传-环境互作是植物表型变异的主要来源。这使得目标环境的育种异常具有挑战性，因为未来的环境条件主要是不确定的。为了实现快速的遗传增益，育种计划需要在有限的测试年内推进基因型。因此，在育种的早期阶段，许多栽培品种在没有在天气条件下测试的情况下被选择（例如，干旱、寒冷或高温胁迫）可能严重影响其表现。为了解决长期存在的在很大程度上不确定的天气条件下预测品种表型的问题，该项目将开发一个计算机模拟平台，将田间试验数据，DNA序列和历史天气记录整合到模型中，使研究人员和育种人员能够预测可能的天气条件下的植物表型。 一个由数据科学家、玉米遗传学家和育种家组成的跨学科团队将使用反应规范模型和深度学习（一种已被证明在捕捉高维数据中的复杂模式方面非常有效的方法），从基因组到领域（G2 F）项目的DNA序列、环境协变量和表型数据中学习G×E模式，该项目是最大的公开发起和领导的G×E研究计划。这些模型将与历史天气数据一起使用，模拟和预测美国许多地方不同玉米杂交种的玉米表现，然后将模拟数据用于揭示G×E的遗传区域，并使用受控天气条件下试验产生的数据验证结果。关于更广泛的影响，该项目将为学生和博士后研究员提供数据科学和基因组学的跨学科培训，并将利用现有的计划为高中生和公众提供外展服务。 所有项目成果都将通过一个专门的项目数据库和公开的长期储存库提供。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A fast algorithm to factorize high-dimensional Tensor Product matrices used in Genetic Models

一种用于分解遗传模型中使用的高维张量积矩阵的快速算法

• DOI: 10.1093/g3journal/jkae001
• 发表时间: 2024
• 期刊: Genetics
• 影响因子: 3.3
• 作者: Lopez-Cruz, Marco;Pérez-Rodríguez, Paulino;de los Campos, Gustavo
• 通讯作者: de los Campos, Gustavo