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

• 批准号: 10172929
• 负责人: MICHAEL Chiao-An WU
• 金额: $ 17.56万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10172929
• 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; 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; kernel methods; 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

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.

项目摘要 就像人类基因组计划创造了无价的基因组图谱一样， 是开发最终通过微生物组构建综合遗传和代谢组学的方法 关系图。这些地图将是一种宝贵的资源，有助于我们更好地了解 微生物和组学特征影响人类疾病和病症的潜在机制， 潜在地导致新的治疗靶点的鉴定。为此目的，本建议力求发展 统计和计算工具，用于绘制微生物和其他生物之间的关联和相互作用， 组学特征以及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，这是由于研究了阴道微生物组和其他组学在分娩中的作用， 结果和更年期，我们的目标是发展统计方法学（1）映射遗传变异， 影响微生物组的组成，以便了解微生物组的固有组成部分， 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，这将有助于了解扰动如何影响系统 并确定治疗靶点的关键通路;（3）利用其他组学技术构建更准确的 基于微生物组的早产预测模型;（4）开发、分发和支持软件 建议的方法。这些方法是基于我们有相当多的框架， 经验，但新的技术贡献，以适应功能的数据，如 种群分层和遗传学相关性，系统发育结构和组成，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 方法有可能加速机制和翻译微生物组研究， 资源，使许多生物学，临床和公共卫生问题， 几十年来一直困扰着研究人员。