Understanding Genetic Basis of Dental Caries via Integrative Genomic Approaches

描述(申请人提供)：尽管有不同的策略来改善行为因素，龋齿(龋齿)仍然是最常见的口腔疾病之一，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，最近的研究也提供了令人信服的证据，证明遗传在龋齿的病因中也起着重要作用。然而，到目前为止，与在其他复杂疾病或特征方面所做的大量努力相比，关于龋病的遗传学研究仍处于早期阶段。在这项建议中，为了补充传统的单标记/基因，我们将开发创新的策略，在全基因组关联研究(GWAS)数据集中识别与龋齿具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新的统计方法来识别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层次化的方式对一条路径内的所有基因和SNPs进行建模，这将提供跨同一路径中的基因借用信息的能力。(2)开发了一种新的密集模块搜索算法，用于识别人类蛋白质相互作用(PPI)网络中关联信号丰富的基因和基因模块(子网络)。除了增强能力外，识别出的子网络还将使我们能够通过相互连接许多疾病基因来检测在蛋白质网络中发挥核心作用的弱关联基因。(3)对AIMS 1和AIMS 2确定的龋齿基因以及其他遗传和基因组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。我们将把AIMS 1和AIMS 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、组织和整理已识别的基因，以及基于连锁扫描、基因表达和文献搜索的先前研究的基因，然后开发基于证据的多维方法，为未来的验证和后续生物信息学分析确定这些基因的优先顺序。该项目的成功完成将为我们提供重要的工具，用于对当前和未来的龋病(以及其他复杂疾病)的GWAs进行综合基因组分析，为龋病研究提供一个用户友好的在线系统，并为未来的验证提供一个优先候选基因的清单。 公共卫生相关性：龋齿(龋齿)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。在这一建议中，为了补充传统的单标记/基因，我们结合统计学、生物信息学和遗传学来开发综合基因组学方法，以在日内瓦龋齿全基因组关联研究(GWAS)数据库中识别具有丰富关联信号的功能相关基因组。这个项目的成功完成将大大加强我们对龋齿和其他牙科疾病的遗传结构的了解，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.