ECA-PGR: Dissecting Natural Mechanisms for Genome Content Variation and the Impact on Phenotypic Variation

植物或动物的性质在一定程度上是由其基因组中的DNA含量决定的。人们可能会认为，这种传递信息的重要作用会在基因组中世代保存下来。事实上，众所周知，基因组是不稳定的，组成基因和非基因的含量可能会随着时间的推移而变化；基因可能会被复制、丢失或轻微改变，这种变化对个体的外表和功能有重大影响。例如，玉米植株中基因含量的变化会影响其生长或重要的农艺性状，如抗旱性、种子大小或产量。个体中基因含量的变化让科学家们感到困惑，但显然对育种者有利：基因含量是如何以及何时发生变化的？它对植物的性状或表型有什么影响？是否可以利用这一过程来确定农业改良的新特征？这项研究项目使用玉米作为模式作物来回答这些问题。这项研究之所以可能，是因为玉米基因组在密切相关的品系中差异很大，而且有大量的遗传资源可以用来测试基因组内容如何变化、何时变化以及为什么变化。该项目将识别基因组变化的特征，并将这些变化与新的特征联系起来。在这一过程中，将从多个教育层面对新参与者进行培训。具体努力将侧重于吸引年轻女孩和妇女投身科学事业。学生和教师将从事一种与农业成果有明确联系的植物基因组学真实研究。还将通过数据库和公共门户网站整理和传播女性科学家的指导和培训机会。该项目将以玉米为模型系统，系统地描述一组不同基因类型之间基因组内容差异的程度，通过基因组签名确定导致基因组内容差异的遗传机制，并衡量对表型差异的影响。该研究计划整合了基因组学、代谢组学、数量遗传学和统计遗传学，以加深我们对基因组内容变异和机制起源在表型结果中所起作用的理解。具体地说，该项目将1)使用从头测序组合和使用长短阅读测序技术相结合的外显子组捕获来识别玉米自交系之间的基因组内容变异，2)识别在全基因组范围内阐明基因组内容变异起源的机制特征，3)实施基因组广泛关联研究，以识别与不同的玉米自交系中的数量、质量、必要和可有可无的表型和化学类型(表型和籽粒含量)类别相关的基因组内容变异，以及4)使用统计遗传学方法来确定造成基因组内容变异的机制和表型结果之间是否存在关系。该项目还为高中、本科生和研究生、博士后助理以及主要是本科院校的教师提供代谢组学、基因组学和统计遗传学方面的新工具和新技术的指导和培训机会。

The nature of a plant or animal is defined in part by the DNA content in its genome. One might expect that this important role of imparting information is preserved in genomes over generations. In fact, genomes are known to be unstable and the constituent gene and non-gene content can change over time; genes can be copied, lost, or altered slightly, and such variation has significant impact on the appearance and function of an individual. As an example, the change in gene content in a corn plant can affect its growth or important agronomic traits, such as drought resistance, seed size or yield. The variation in gene content in individuals is puzzling to scientists and clearly beneficial to breeders: How and when does gene content change? What impact does it have on the traits, or phenotype, of the plant? And can the process be harnessed to identify new traits for agricultural improvement? This research project uses corn, or maize, as a model crop to answer these questions. The research is possible because the maize genome is remarkably variable in closely related lines, and there are extensive genetic resources that can be used to test how, when and why genome content changes. The project will identify signatures of genome change and will associate these changes to new traits. In the process, new participants will be trained from many educational levels. Specific efforts will focus on attracting young girls and women to scientific careers. Students and teachers will engage in a type of authentic research in plant genomics that has clear connections to outcomes in agriculture. Mentoring and training opportunities for female scientists will also be collated and disseminated through databases and public web portals.Using maize as a model system, this project will systematically characterize the extent of genome content variation among a panel of diverse genotypes, identify the genetic mechanisms responsible for this variation in genome content through genomic signatures, and measure the impact on phenotypic variation. The research plan integrates genomics, metabolomics, quantitative genetics, and statistical genetics to further our understanding of genome content variation and the role mechanistic origin plays in phenotypic outcomes. Specifically, this project will 1) identify genome content variation between maize inbred lines using a combination of de novo genome assemblies and exome capture using a combination of short- and long-read sequencing technologies, 2) identify mechanistic signatures that elucidate the origin of genome content variation on a genome-wide scale, 3) implement Genome Wide Association Studies to identify genome content variation associated with quantitative, qualitative, essential, and dispensable phenotypic and chemotypic (surface lipid profiles and kernel content) classes of traits in a diverse panel of maize inbred lines, and 4) use statistical genetic approaches to determine if there is a relationship between the mechanisms that create genome content variation and phenotypic outcomes. The project also provides mentoring and training opportunities in new tools and technologies in metabolomics, genomics, and statistical genetics for high school, undergraduate and graduate students, postdoctoral associates, and faculty at primarily undergraduate institutions.