Joint Analysis of Microbiome and Other Genomic Data Types

项目总结 就像人类基因组计划创建了无价的基因组图谱一样，这个计划的目标是 是开发最终利用微生物组构建完整的遗传和代谢组学的方法 关系图。这些地图将是一个宝贵的资源，以增进我们对 微生物和组学特征影响人类疾病和状况的潜在机制， 有可能导致新的治疗靶点的确定。为达到这些目的，这项提案试图发展 统计和计算工具，用于绘制微生物和其他- 基因组学特征，并进一步利用其他组学来改进基于微生物组的预测模型。 具体地说，研究的动机是研究阴道微生物组和其他组学在分娩中的作用 结果和更年期，我们的目标是开发统计方法来(1)绘制遗传变异图， 影响微生物组的组成，以了解微生物组的固有成分以及 学习遗传学影响结果的机制；(2)创建整合两者的全球代谢图 微生物和代谢物，这将使人们能够理解扰动可能如何影响系统 并确定治疗靶点的关键途径；(3)利用其他组学构建更准确的 基于微生物组的早产预测模型；(4)软件的开发、分发和支持 所提议的方法的包。这些方法是基于框架的，在这些框架中我们有相当多的 经验，但做出了新的技术贡献，以适应数据的特征，如 种群分层和遗传、系统发育结构、组成以及 实际考虑因素，如样本和其他组学数据的可用性。因此，这些新的 这些方法有可能加速机械性和转译微生物组的研究，开发重要的 能够系统地解决许多生物、临床和公共卫生问题的资源 几十年来一直躲避着研究人员。

PROJECT SUMMARY In the same way that the human genome project created invaluable genomic maps, the objective of this project is to develop methods for eventual construction of comprehensive genetic and metabolomic by microbome relationship maps. Such maps would be an invaluable resource for improving our understanding as to the underlying mechanisms by which microbes and –omics features influence human diseases and conditions, potentially leading to identification of novel therapeutic targets. To these ends, this proposal seeks to develop statistical and computational tools for mapping associations and interactions between microbes and other – omic features and for further utilizing other –omics to improve microbiome based prediction models. Specifically, motivated by studies examining the role of the vaginal microbiome and other –omics in birth outcomes and menopause, we aim to develop statistical methodology for (1) mapping genetic variants that influence microbiome composition so as to understand the innate component of the microbiome as well as learn mechanisms by which genetics influence outcomes; (2) creating global metabolic maps integrating both microbes and metabolites which will enable understanding of how perturbations might influence the system and identify key pathways for therapeutic target; (3) exploiting other –omics in constructing more accurate microbiome based prediction models for preterm birth; (4) developing, distributing and supporting software packages for the proposed methods. The methods are based on frameworks in which we have considerable experience, but novel technical contributions are made to accommodate features of the data such as population stratification and relatedness in genetics, phylogenetic structure, and compositionality, as well as practical considerations such as availability of samples and other –omics data. Consequently, these new methods have the potential for accelerating mechanistic and translational microbiome studies, developing vital resources for enabling systematic achievement of many biological, clinical, and public health problems that have eluded researchers for decades.