Understanding Genetic Basis of Dental Caries via Integrative Genomic Approaches

描述（由申请人提供）：尽管改善行为因素的策略不同，但龋齿（蛀牙）仍然是最普遍的口腔疾病之一，也是一个远未得到控制的具有挑战性的公共卫生问题。除了环境因素外，最近的研究提供了令人信服的证据，表明遗传学在龋齿的病因学中也起着重要作用。然而，迄今为止，与在其他复杂疾病或特征方面所做的大量努力相比，对龋齿的遗传研究仍处于早期阶段。在这个提议中，为了补充传统的单一标记/基因，我们将开发创新的策略，以确定在全基因组关联研究（GWAS）数据集中与龋齿有丰富关联的功能相关基因组。我们的具体目标如下。(1)发展一种基于混合效应模型的统计方法，以识别GWAS中具有丰富关联信号的基因和基因集。我们将使用随机基因效应以分层方式对通路中的所有基因和SNP进行建模，这将提供在同一通路中跨基因借用信息的能力。(2)提出一种新的密集模块搜索算法，用于在人类蛋白质-蛋白质相互作用（PPI）网络中识别具有丰富关联信号的基因和基因模块（子网络）。除了增强功能外，所识别的子网络还将使我们能够通过互连许多疾病基因来检测在蛋白质网络中发挥核心作用的弱相关基因。(3)对目标1和目标2所确定的龋齿基因以及其他遗传学和基因组学研究所涉及的基因进行综合分析，并通过用户友好的网络界面公开所有数据。我们将在目标1和2中开发的方法应用于日内瓦龋齿GWAS数据集（dbGap登录号：phs000095.v1.p1）。然后，我们将收集，组织和策划所确定的基因，沿着那些从以前的研究的基础上连锁扫描，基因表达，和文献检索，然后开发多维循证方法，优先考虑这些基因，为未来的验证和后续的生物信息学分析。该项目的成功完成将为我们提供重要的工具，用于对龋齿（以及其他复杂疾病）中当前和未来的GWAS进行综合基因组分析，为龋齿研究提供一个用户友好的在线系统，并为未来验证提供优先候选基因列表。 公共卫生相关性：龋齿（蛀牙）仍然是最普遍的口腔疾病之一，并且是远未得到控制的具有挑战性的公共卫生问题。在这个建议中，以补充传统的单一标记/基因，我们结合联合收割机统计学，生物信息学和遗传学，开发整合基因组学方法，以确定在日内瓦龋齿全基因组关联研究（GWAS）数据集丰富的关联信号的功能相关的基因组。该项目的成功完成将大大提高我们对龋齿和其他牙科疾病的遗传结构的理解，并将导致更有效的预防和治疗策略。

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. PUBLIC HEALTH RELEVANCE: 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 this proposal, to complement the traditional single marker/gene, we combine statistics, bioinformatics, and genetics to develop integrative genomics approaches to identify groups of functionally related genes with enriched association signals in the GENEVA dental caries genome-wide association studies (GWAS) dataset. Successful completion of this project will significantly enhance our understanding of the genetic architecture underlying caries as well as other dental diseases and will lead to more effective prevention and treatment strategies.