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

茎秆倒伏问题（收获前植物茎秆断裂）每年使美国粮食产量减少约20%。尽管有机会极大地改善粮食安全，但导致秸秆倒伏的机制尚未确定。这个名为“理解基因组和表型之间的关系”的RII Track-2 FEC FY 2018项目汇集了来自数学，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。肯塔基大学、爱达荷大学和克莱姆森大学这三所跨司法管辖区的大学利用他们的研究专长创建了肯塔基/爱达荷/克莱姆森植物生物力学联盟（KIC-Consortium）。该联盟将结合数学模型和创新技术，在大量植物种群中测试茎秆强度，以预测导致茎秆倒伏的潜在特征，并可能用于克服茎秆倒伏。今后将系统地利用这些特征改良粮食作物品种；因此，有助于粮食和能源安全。该项目将为7名早期职业教师提供早期就业机会和跨学科研究机会，并通过几个培训项目在该领域建立强大的智力基础设施。技术描述该项目将重点关注秸秆倒伏问题，这是一个困扰农业（尤其是玉米和高粱）数十年的问题，并限制了国家和全球的粮食安全。茎秆倒伏是作物收割前茎秆的断裂，每年可使世界粮食产量减少20%。仅提高玉米茎秆倒伏率1%，每年就能增加200亿磅（约20亿美元）的产量。茎秆抗倒伏的遗传调控已被证明是难以捉摸的。虽然目前可用的最先进的基因组学方法，如基因组全关联（GWAS）、基因共表达网络和表达定量位点（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、非线性相互作用和功能关系，将提供基因组与表型关系的全面图景。由美国肯塔基大学、爱达荷大学和克莱姆森大学组成的跨区域植物生物力学联盟（KIC-Consortium）主要研究基因组与表型性状之间的关系是由存在于多个时空尺度上的复杂功能关系和非线性相互作用所控制的。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、有限元方法和中间表型数据，这些数据通常被排除在基因组到表型图谱的绘制工作之外。该团队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，以适应和推进每个管辖区？美国研究的未来。该奖项反映了美国国家科学基金会的法定使命，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。

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.