SGER: Connecting the Transcriptome and Metabolome with Natural Genetic Variation.

PI：Daniel J.Kliebenstein(加州大学戴维斯分校)智力价值：最近的技术进步使基因表达、代谢物积累和蛋白质积累的大规模并行分析成为可能。这些“组学”方法产生生物推理的能力面临两个限制。第一个问题是如何将不同的组学数据集结合成一个单一的分析。其次，大量的未知基因和代谢物限制了对任何给定数据集的潜在推断。这个项目的主要目标是测试是否可以使用自然遗传变异来整合代谢组学和转录组数据集，并确定未知代谢物和酶之间的联系。具体的代谢物QTL分析将使用拟南芥Bay-0X Sa重组自交系群体以及野生型材料来开发和测试整合代谢组学和转录组分析的方法，使用遗传协方差而不是代谢物和转录物协方差来连接这些组学水平的数据集。所有的计算方法将首先用已知的代谢物和酶编码基因进行开发，然后使用未知的代谢物和酶编码基因进行测试。除了促进开发整合不同组学水平的数据集的方法外，使用自然遗传变异还将允许通过结合转录组学和代谢组学进行QTL分析来测试遗传变异和表型变异之间的联系。预期的成果包括产生可直接适用于转录组和代谢组学可行的任何物种的技术，以及将被所有植物生物学家普遍使用的植物代谢变异数据库，并有可能提高在模式植物系统中QTL识别和验证的速度。更广泛的影响：由于使用的方法是基于检测和测量代谢物和转录本的能力，本项目开发的所有统计方法和理论基础在任何可以测量代谢物和转录本的生物系统中都将是有用的。此外，通过研究遗传变异和表型变异之间的联系，该项目可能对整个生物科学产生更广泛的影响，这在农业和医学中尤其重要，在农业中，植物和动物的改良依赖于遗传和表型变异，而在医学中，许多疾病依赖于改变新陈代谢的遗传变异。最后，该项目将能够对研究生进行实验设计、技术细节和分析方面的培训，以获得和审问大型新陈代谢数据集。这些技能对于后基因组研究人员的成功是不可或缺的，他们试图通过使用大规模基因组数据集来生成和测试假说。

PI: Daniel J. Kliebenstein (University of California, Davis)Intellectual Merit:Recent technological advancements have enabled massively parallel analysis of gene expression, metabolite accumulation and protein accumulation. The ability of these "-omics" approaches to generate biological inference faces two limitations. The first is how to combine the different -omics datasets into a single analysis. Secondly, the high number of unknown genes and metabolites limit the potential inference from any given dataset. The primary goal of this project is to test whether natural genetic variation can be used to integrate Metabolomics and Transcriptomics datasets and identify linkages between unknown metabolites and enzymes. Specifically metabolite QTL analysis will be performed using the Arabidopsis Bay-0 X Sha recombinant inbred line population as well as wildtype accessions to develop and test methods for integrating Metabolomics and Transcriptomics analysis using genetic co-variance, rather than metabolite and transcript co-variance, to link these omics-level datasets. All computational approaches will first be developed with known metabolites and enzyme encoding genes and then tested using the unknown metabolites and enzyme encoding genes.In addition to facilitating the development of approaches towards integrating different omics-level datasets, the use of natural genetic variation will also allow for the testing of the link between genetic variation and phenotypic variation by combining transcriptomics and metabolomics for QTL analysis. Expected outcomes include the generation of techniques that will be directly applicable to any species in which transcriptomics and metabolomics are feasible and a database of plant metabolic variation that will be of general use to all plant biologists and can potentially enhance the rate of QTL identification and validation within a model plant system. Broader Impacts: Because the approaches used are based on the ability to detect and measure metabolites and transcripts, all statistical methodology and theoretical underpinnings developed in this project will be useful in any biological system where metabolites and transcripts can be measured. In addition, the project has potential broader impact for the biological sciences in general through the study of the link between genetic variation and phenotypic variation, of particular importance in Agriculture, where plant and animal improvement relies upon genetic and phenotypic variation, and Medicine, where numerous diseases are dependent upon genetic variation that alters metabolism. Finally the project will enable the training of a graduate student in the experimental design, technical details and analysis required to obtain and interrogate large metabolomic datasets. These skills will be integral for the success of post-genomic researchers attempting to generate and test hypotheses through the use of large-scale genomic datasets.