Collaborative Research: DMS/NIGMS 2: New statistical methods, theory, and software for microbiome data

合作研究：DMS/NIGMS 2：微生物组数据的新统计方法、理论和软件

基本信息

批准号：
10797410
负责人：
Lingzhou Xue
金额：
$ 29.6万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-05 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10797410
关键词：
16S ribosomal RNA sequencing Asthma Biological Clinical Communities Complex Computer software Data Dependence Diabetes Mellitus Diagnostic Disease Foundations Genes Growth Health High-Throughput Nucleotide Sequencing Human Infant Intervention Learning Maps Mediation Metagenomics Methodology Molecular Epidemiology National Institute of General Medical Sciences Nature Obesity Outcome Pattern Phylogenetic Analysis Play Prevention Research Research Personnel Role Sample Size Shotgun Sequencing Statistical Methods Structure Surveys Technology Therapeutic analytical tool autism spectrum disorder biomarker discovery cancer biomarkers combat computerized tools data structure design disease diagnosis genome wide association study high dimensionality improved insight interest metabolomics microbial microbial community microbiome microbiome analysis microbiome research prognostic skin disorder software development theories

项目摘要

Advancement in high-throughput sequencing technology allows the characterization of the microbiome via either marker-gene (e.g., 16S rRNA gene) amplicon sequencing or metagenomics shotgun sequencing. Consequently, the scientific community is increasingly appreciative of the important role that the microbiome community plays in many human health and disease conditions. Despite its popularity, the field of microbiome and metagenomics studies, however, has not yet reached the maturity attained in other established molecular epidemiology fields, such as cancer biomarker discovery and genome-wide association studies for making the leap from omics survey to rational microbiome-based therapeutics. One of the primary limitations to leveraging this large body of microbiome and metagenomics data is computational and statistical challenges. Among these is the technical nature of the data, including high dimensionality, sparse count or compositional data structure, relatively small sample size, and complex dependence/correlation structure such as phylogenetic relatedness. To combat these challenges, this proposal seeks to develop statistical methods, theory, and computational tools to accurately characterize microbial communities within and across large studies while maintaining both statistical rigor and biological relevance. This project develops new statistical methods, theory, and software to characterize microbial communities within and across large studies accurately. Specifically, motivated by biomedical and biological problems encountered in microbiome studies of skin diseases, autism spectrum disorder, and infant growth, the investigators will develop statistical methodology for (1) mapping microbial taxa that influence clinical outcomes of interest in a powerful and robust pattern; (2) learning the correlation structure among microbial taxa to decode the complex networks and interactions among the microbiome community; (3) a new mediation analysis for microbiome studies with high-dimensional microbial profiles and other omics profiles such as metabolomics. Successful completion of this proposal will fill the gap between the burgeoning research interests in microbiome studies and the need for more analytical tools. This proposal will improve the understanding of the underlying microbiome mechanism of many health and disease conditions, which is critical to designing microbiome-based interventions for prognostic, diagnostic, and treatment purposes.

高通量测序技术的进步允许通过以下方式表征微生物组标记基因（例如 16S rRNA 基因）扩增子测序或宏基因组鸟枪测序。因此，科学界越来越认识到科学技术的重要作用。微生物群落在许多人类健康和疾病状况中发挥着作用。尽管它很受欢迎，然而，微生物组和宏基因组学研究领域尚未达到成熟度其他已建立的分子流行病学领域，例如癌症生物标志物发现和全基因组关联研究，实现从组学调查到基于微生物组的合理治疗的飞跃。利用大量微生物组和宏基因组数据的主要限制之一是计算和统计挑战。其中包括数据的技术性质，包括高维数、稀疏计数或组合数据结构、样本量相对较小且复杂依赖性/相关结构，例如系统发育相关性。为了应对这些挑战，这该提案旨在开发统计方法、理论和计算工具来准确表征大型研究内部和之间的微生物群落，同时保持统计严谨性和生物学相关性。该项目开发新的统计方法、理论和软件来表征准确地了解大型研究内部和之间的微生物群落。具体来说，受到生物医学的推动以及皮肤病、自闭症谱系障碍等微生物组研究中遇到的生物学问题，和婴儿生长，研究人员将开发统计方法用于（1）绘制微生物分类群，以强大而稳健的模式影响感兴趣的临床结果； (2)学习相关性微生物类群之间的结构，以解码微生物组之间的复杂网络和相互作用社区; （3）高维微生物谱的微生物组研究的新中介分析以及其他组学概况，例如代谢组学。该提案的成功完成将填补空白对微生物组研究的新兴研究兴趣和对更多分析工具的需求之间的关系。该提案将增进对许多健康的潜在微生物组机制的理解和疾病状况，这对于设计基于微生物组的预后干预措施至关重要，诊断和治疗目的。