EPIDEMIOLOGY/GENE MAPPING OF EARLY ONSET PERIODONTITIS

项目2：早发性牙周炎的遗传流行病学和分子基因图谱研究大量证据表明，个体之间的遗传差异影响早发性牙周炎(EOP)的风险。然而，这种疾病显然有复杂的病因。可遗传变异和环境因素(如吸烟、卫生、接触病原体)似乎在决定疾病风险方面相互作用。我们先前的研究表明白介素1基因多态与EOP的易感性有关。其他可能的易感基因被定位到染色体区域，但尚未明确。我们还启动了与EOP相关的表型的基因图谱研究，如吸烟行为和血清免疫球蛋白。人类分子基因组学和统计遗传流行病学的最新进展为人类分子基因组学提供了极大的动力，统计遗传流行病学为识别与EOP等复杂性状相关的基因提供了极大的动力。我们建议应用这些工具和方法来跟进我们之前的EOP发现：i)通过分析额外的IL-1多态和额外的EOP家系和病例对照受试者来验证IL-1在EOP中的作用；ii)使用额外的EOP家系复制我们的其他基因图谱，通过突变筛查识别疾病基因，评估新的候选基因多态，当检测技术成熟时，执行全基因组不平衡定位；iii)分析危险因素表型，如吸烟和免疫球蛋白水平，作为EOP分析的协变量和高度可遗传的直接感兴趣的特征；开发和评估EOP的定量测量方法，以取代当前离散的疾病状态分类，并使用这些数量性状通过方差成分分析和其他数量性状基因座(QTL)方法重新分析我们的基因图谱数据。

Project 2: Genetic Epidemiology and Molecular Gene Mapping Studies of Early Onset Periodontitis Substantial evidence indicates that genetic differences among individuals influence risk of early onset periodontitis (EOP). However, this disease clearly has a complex etiology. Both heritable variation and factors in the environment (e.g., smoking, hygiene, exposure to pathogens) appear to interact in determining disease risk. Our previous studies demonstrated an association or interleukin-1 polymorphisms and EOP susceptibility. Other possible susceptibility genes were mapped to chromosomal regions but not yet specifically identified. We also initiated gene mapping studies of EOP-related phenotypes such as smoking behavior and serum immunoglobulins. Recent major advances in human molecular genomics and statistical genetic epidemiology provide greatly increased power in human molecular genomics and statistical genetic epidemiology provide greatly increased power to identify genes underlying complex traits such as EOP. We propose to apply these tools and approaches to follow up our previous findings for EOP by: i) verifying the role of IL-1 in EOP through analyses of additional IL-1 polymorphisms and additional EOP families and case-control subjects; ii) replicating our other gene mapping EOP findings using additional EOP families, identifying disease genes via mutational screening, evaluating new candidate gene polymorphisms and, when assay technologies mature, performing genome-wide disequilibrium mapping; iii) analyzing risk factor phenotypes such as smoking and immunoglobulin levels, both as covariates for EOP analyses and as highly heritable traits of direct interest; and iv) developing and evaluating quantitative measurements of EOP in lieu of the current discrete disease state classification and using these quantitative traits for re-analyses of our gen mapping data with variance components analyses and other quantitative trait locus (QTL) methods.