Joint Analysis of Microbiome and Other Genomic Data Types

微生物组和其他基因组数据类型的联合分析

基本信息

批准号：
9763572
负责人：
MICHAEL Chiao-An WU
金额：
$ 39.6万
依托单位：
FRED HUTCHINSON CANCER RESEARCH CENTER
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9763572
关键词：
Achievement Address Area Automobile Driving Biological Birth Chromosome Mapping Clinical Complex Computer software Data Data Set Development Early Diagnosis Future Genes Genetic Genomics Grouping Health Human Genome Project Joints Lasso Learning Machine Learning Maintenance Maps Medical Menopause Metabolic Methodology Methods Microbe Modeling Modification Molecular Target Network-based Outcome Pathway interactions Performance Phylogenetic Analysis Pregnancy Outcome Premature Birth Procedures Public Health Quantitative Trait Loci Research Personnel Resolution Resources Risk Role Sampling Scientific Advances and Accomplishments Structure System Taxonomy Training Woman Work base biological systems computerized tools experience genetic variant genomic data human disease improved interest loss of function metabolomics microbial microbial community microbiome microbiome analysis microbiome components microbiome composition microbiome research new therapeutic target novel open source population stratification predictive modeling simulation software development therapeutic target tool translational study user friendly software vaginal microbiome

项目摘要

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.

项目概要就像人类基因组计划创建了宝贵的基因组图谱一样，该项目的目标开发通过微生物组最终构建综合遗传和代谢组学的方法这样的关系图对于增进我们对事物的理解是非常宝贵的资源。微生物和组学特征影响人类疾病和状况的潜在机制，为此，该提案旨在开发新的治疗靶点。用于绘制微生物与其他物质之间的关联和相互作用的统计和计算工具 – 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。具体来说，受到研究阴道微生物组和其他组学在出生中的作用的启发结果和更年期，我们的目标是开发统计方法来（1）绘制遗传变异，这些变异影响微生物组的组成，以便了解微生物组的固有成分以及了解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将有助于了解扰动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方法基于微生物组的早产预测模型；(4) 开发、分发和支持软件；所提出的方法的包基于我们拥有大量的框架。经验，但做出了新颖的技术贡献来适应数据的特征，例如遗传学、系统发育结构和组成方面的群体分层和相关性，以及实际考虑因素，例如样本和其他经过测试的组学数据的可用性，这些新的。这些方法有可能加速微生物组的机械和转化研究，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困扰着研究人员。