Novel statistical methods for controlled variable selection of microbiome data

微生物组数据受控变量选择的新统计方法

• 批准号: 10116262
• 负责人: Vernon M Chinchilli
• 金额: $ 22.57万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10116262
• 关键词: 16S ribosomal RNA sequencing; Address; Bacterial Vaginosis; Biological; Characteristics; Clinical; Communities; Complex; Computer software; Conflict (Psychology); Data; Data Analyses; Detection; Development; Disease; Disease Outcome; Ensure; Environment; Equilibrium; Etiology; Formulation; Genetic; Genome; Genomics; Goals; HIV risk; Health; Human; Human Microbiome; Individual; Integration Host Factors; Investigation; Journals; Laboratories; Lead; Light; Literature; Metabolite Interaction; Methodology; Methods; Modeling; Monitor; Obesity; Outcome; Pathology; Peer Review; Phenotype; Phylogenetic Analysis; Play; Prevention; Prevention strategy; Publications; Reproducibility; Research; Research Design; Research Personnel; Role; Sample Size; Shotguns; Signal Transduction; Solid; Specific qualifier value; Statistical Data Interpretation; Statistical Methods; Structure; Subgroup; Techniques; Work; analytical tool; bacterial community; bacteriome; base; computerized tools; disorder prevention; effective therapy; gene environment interaction; high dimensionality; improved; innovation; interest; metabolome; metabolomics; metagenomic sequencing; microbial; microbiome; microbiome analysis; microbiome research; non-alcoholic fatty liver disease; novel; open source; preventive intervention; response; symposium; tool; treatment strategy; user-friendly

Project Summary/Abstract The scientific community is increasingly appreciative of the important role that the microbiome community plays in many diseases and health conditions. The structure of the microbiome community (e.g., relative abundances of different taxa and microbial network/interactions) is subject to change in response to many environment and host factors. Scientific investigation of how microbiome interact with each other, with their environment and with their host can shed light on our understanding of the underlying biological mechanism of microbiome-related disease and health conditions. Despite the incredible amount of research interest and availability of massive data through the innovative use of cutting-edge techniques (16S rRNA gene sequencing, shotgun metagenomics sequencing and metabolomics), there are still insufficient statistical tools that can fully handle the complexity of microbiome data, including the high-dimensionality, phylogenetic relatedness, relatively small sample size, compositional constraint and others. The main goal of this proposal is to develop statistically powerful and computationally efficient methods to address these challenges in analyzing microbiome data. In particular, this research will be applied to high-throughput microbiome data and lead to new statistical controlled variable selection methods that 1) select a subgroup of taxa that are genuinely associated with disease-related outcomes under a pre-specified false discovery rate (FDR), where the outcomes can be either a single disease outcome of interest or multivariate such as multiple secondary phenotypes related to the disease; and b) identify taxa and taxa-metabolite interactions that are associated with a disease outcome under a certain FDR threshold. Our proposed methods are innovative in that it can both select important taxa features or taxa-metabolites interactions and have the FDR being controlled, which largely enhances the reproducibility and reliability of the discovery results in microbiome association studies. The enhanced taxa selection would further facilitate downstream laboratory-based functional studies, eventually leading to potential improvements in prevention, detection, treatment and monitoring of many health and disease conditions from a microbiome's perspective. Completion of this proposal will also help bridging the gap between the burgeoning research interest in microbiome studies and the lack of analytical tools. In addition to publication in peer-reviewed journals, we will make our results disseminated through conferences and open-source software that is freely available to the wider scientific community. The proposed methods are essential for improved understanding of microbiome mechanism along with its interaction with host genome or metabolome in the pathology of certain diseases, which are of central importance to human health.

项目总结/文摘

项目成果

Variant-set association test for generalized linear mixed model.

• DOI: 10.1002/gepi.22378
• 发表时间: 2021-06
• 期刊: Genetic epidemiology
• 影响因子: 2.1
• 作者: Zhan X;Banerjee K;Chen J
• 通讯作者: Chen J