Understanding Genetic Basis of Dental Caries via Integrative Genomic Approaches

通过综合基因组方法了解龋齿的遗传基础

基本信息

批准号：
8320126
负责人：
Lily Wang
金额：
$ 23.2万
依托单位：
VANDERBILT UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-01 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8320126
关键词：
Algorithms Architecture Behavioral Bioinformatics Candidate Disease Gene Complement Complex Data Data Analyses Data Set Databases Dental caries Disease Disease Association Environmental Risk Factor Etiology Family Future Gene Expression Genes Genetic Genomics Human Individual Label Lead Literature Methods Modeling Mouth Diseases Online Systems Oral health Other Genetics Overlapping Genes Pathway interactions Pattern Play Prevention strategy Property Proteins Public Health Research Research Personnel Resistance Role Sampling Scanning Signal Transduction Site Staging Statistical Methods Testing Tooth Diseases Validation base database of Genotypes and Phenotypes design evidence base experience flexibility follow-up functional group genome wide association study genome-wide improved innovation novel protein protein interaction statistics success text searching tool trait treatment strategy user-friendly web interface

项目摘要

DESCRIPTION (provided by applicant): Despite different strategies for improving behavioral factors, dental caries (tooth decay) remains to be one of the most prevalent oral diseases and a challenging public health problem far from being controlled. In addition to environmental factors, recent studies have provided convincing evidence that genetics also plays an important role in the etiology of dental caries. However, to date, genetic studies on caries are still in an early stage compared to numerous efforts that have been made in other complex diseases or traits. In this proposal, to complement the traditional single marker/gene, we will develop innovative strategies to identify groups of functional related genes with enriched associations with dental caries in genome-wide association studies (GWAS) dataset. Our Specific Aims are as follows. (1) To develop a novel statistical method based on mixed effects models to identify genes and gene sets that have enriched association signals in GWAS. We will model all the genes and SNPs within a pathway in a hierarchical fashion using random gene effects, which will provide the ability to borrow information across genes in the same pathway. (2) To develop a novel dense module searching algorithm for identifying genes and gene modules (subnetworks) with enriched association signals on the human protein-protein interaction (PPI) networks. In addition to increased power, the identified subnetworks will also enable us to detect weakly associated genes playing central roles in the protein network by interconnecting many disease genes. (3) To perform an integrative analysis for ranking caries genes identified by Aims 1 and 2 and genes implicated by other genetic and genomic studies and to make all the data publicly available via a user-friendly web interface. We will apply the methods developed in Aims 1 and 2 to the GENEVA dental caries GWAS dataset (dbGap accession no: phs000095.v1.p1). We will then collect, organize and curate the genes identified, along with those from previous studies based on linkage scans, gene expression, and literature searches, and then develop multi-dimensional evidence-based approaches to prioritize these genes for future validation and follow up bioinformatics analysis. The successful completion of this project will provide us with important tools for integrative genomic analysis of current and future GWAS in caries (as well as other complex diseases), a user-friendly online system for caries research, and a list of prioritized candidate genes for future validation.

描述（由申请人提供）：尽管改善了行为因素的不同策略，但龋齿（牙齿衰减）仍然是最普遍的口腔疾病之一，而远非受到控制的挑战性公共卫生问题。除了环境因素外，最近的研究还提供了令人信服的证据，表明遗传学在龋齿的病因中也起着重要作用。但是，迄今为止，与其他复杂疾病或特征所做的许多努力相比，对龋齿的遗传研究仍处于早期阶段。在此提案中，为了补充传统的单个标记/基因，我们将制定创新的策略，以确定与牙科龋齿在基因组全基因组关联研究（GWAS）数据集中的富集相关基因组。我们的具体目标如下。（1）开发一种基于混合效应模型的新型统计方法，以识别具有富含GWAS关联信号的基因和基因集。我们将使用随机基因效应以层次的方式对所有基因和SNP进行建模，这将提供能够在同一途径中借用跨基因的信息。（2）开发一种新型密集的模块搜索算法，以识别具有人类蛋白质 - 蛋白质相互作用（PPI）网络上富集关联信号的基因和基因模块（子网）。除了增加功率外，确定的子网还将使我们能够通过互连许多疾病基因来检测弱相关的基因在蛋白质网络中起着核心作用。（3）对AIM 1和2识别的排名龋齿基因以及其他遗传和基因组研究涉及的基因进行排名分析，并通过用户友好的Web界面公开获得所有数据。我们将在AIMS 1和2中开发的方法应用于Geneva Dental Caries GWAS数据集（DBGAP登录号：PHS00000095.V1.P1）。然后，我们将根据连锁扫描，基因表达和文献搜索收集，组织和策划所鉴定的基因，以及先前研究的基因，然后开发多维证据的方法，以优先考虑这些基因以进行未来验证并跟进生物信息学分析。该项目的成功完成将为我们提供重要的工具，用于整合龋齿（以及其他复杂疾病），用于龋齿研究的用户友好的在线系统以及优先候选基因列表的当前和未来GWA的集成基因组分析。未来验证。