RII Track-2 FEC: A Multiscale, Multiphysics Modeling Framework for Genome-to Phenome Mapping via Intermediate Phenotypes

非技术性描述茎杆倒伏（收获前植物茎杆的断裂）问题每年使美国谷物产量减少约20%。尽管有机会大大改善粮食安全，但导致秸秆倒伏的机制尚未建立。这个RII Track-2 FEC FY 2018项目名为“了解基因组和表型组之间的关系”，汇集了来自数学，工程，化学，基因组学和生物学的独特团队，为解决这一复杂问题提供了独特的方法。三个跨司法管辖区的大学，肯塔基州大学，爱达荷大学和克莱姆森大学利用他们的研究专长，创建了肯塔基州/爱达荷州/克莱姆森植物生物力学联盟（KIC联盟）。该联盟将联合收割机与创新技术相结合，旨在测试大量植物的茎强度，以预测引起并可用于克服茎倒伏的潜在特征。这些特征将在未来被系统地用于改进粮食作物品种，从而有助于粮食和能源安全。该项目将为七名早期职业教师提供早期职业机会和跨学科研究，并通过几个培训项目在该领域建立强大的知识基础设施。该项目将重点关注秸秆倒伏，这是农业（特别是玉米和高粱）几十年来面临的一个问题，限制了国家和全球的粮食安全。茎杆倒伏是在收获之前植物茎杆的断裂，并且每年可使全球谷物产量减少高达20%。在玉米中，仅将秸秆倒伏改善1%就可以每年增加200亿磅（约20亿美元）的产量。 茎秆抗倒伏的遗传调控是一个谜。虽然目前可用的最先进的基因组学方法，如全基因组关联（GWAS），基因共表达网络和表达定量基因座（eQTL）分析已经成功地将特定基因/变体/转录本与特定表型联系起来，但它们往往低估（缺失遗传力）或高估基因组与表型的相互作用。一个更灵活的数学/统计框架，能够捕捉复杂的多尺度，非线性的相互作用和功能的关系，将提供一个全面的基因组表型的关系。由肯塔基州大学、爱达荷州大学和克莱姆森大学组成的跨辖区植物生物力学联盟（KIC-联盟）的重点是假设基因组和表型性状之间的关系受复杂的功能关系和存在于多个时空尺度的非线性相互作用的支配。为了实现这一假设，KIC联盟将开发一个多尺度、多物理场建模框架，该框架足够灵活，可以解释复杂的非线性相互作用，并利用先进的统计方法、有限元方法和通常被排除在基因组-表型映射工作之外的中间表型数据。该团队已制定了综合的劳动力目标和教育/早期职业招聘和培训目标，以适应和促进每个司法管辖区？该奖项反映了NSF的法定使命，并被认为是值得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的支持。

Non-Technical DescriptionThe problem of stalk lodging (breakage of plant stems prior to harvest) reduces United States grain yields by approximately 20% annually. Despite the opportunity to greatly improve food security, the mechanisms that cause stalk lodging have not been established. This RII Track-2 FEC FY 2018 project entitled "Understanding the relationship between genome and phenome" brings together a unique team from mathematics, engineering, chemistry, genomics, and biology to provide a unique approach to solving this complex problem. Three interjurisdictional Universities, the University of Kentucky, University of Idaho, and Clemson University have leveraged their research expertise to create the Kentucky/Idaho/Clemson Plant Biomechanics Consortium (KIC-Consortium). This consortium will combine mathematical modeling with innovative technology designed to test stem strength on large populations of plants to predict the underlying features that both cause, and may be used to overcome, stem lodging. These features will be systematically used in the future to improve grain crop varieties; thus, aiding in both food and energy security. The project will provide early career opportunities and exposure to interdisciplinary research for seven early career faculty and build a robust intellectual infrastructure in this field through several training programs. Technical DescriptionThis project will focus on stalk-lodging, a problem that has faced agriculture (maize and sorghum in particular) for decades and limits food security nationally and globally. Stalk lodging is the breakage of plant stems prior to harvest and may reduce worldwide grain yields by up to 20% annually. Improving stalk lodging by a mere 1% in maize would lead to a production increase of 20 billion pounds (~$2 billion) each year. The genetic regulation of stalk lodging resistance has proven to be elusive. While currently available state-of-the-art genomics approaches like genome wide association (GWAS), gene co-expression networks, and expression quantitative locus (eQTL) analyses have been successful in linking specific genes/variants/transcripts to specific phenotypes, they tend to either underestimate (missing heritability), or overestimate genome-to-phenome interactions. A more flexible mathematical/statistical framework capable of capturing complex multiscale, nonlinear interactions and functional relationships will provide a comprehensive picture of genome-to-phenome relationships. The proposed interjurisdictional plant biomechanics consortium involving the University of Kentucky, the University of Idaho, and Clemson University (KIC-Consortium) is focused on the hypothesis that the relationship between a genome and phenotypic traits is governed by complex functional relationships and nonlinear interactions existing at multiple temporal and spatial scales. To pursue this hypothesis, the KIC-Consortium will develop a multiscale, multiphysics modeling framework that is flexible enough to account for sophisticated nonlinear interactions and that utilizes advanced statistical methods, finite element methods, and intermediate phenotype data typically excluded from genome-to-phenome mapping efforts. The team has developed integrated workforce goals and education/early career recruitment and training goals to accommodate and advance each jurisdiction?s research future.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.