Survival genetics methods for genetic association studies of early childhood caries

项目摘要 摘要儿童早期龋病是我国学龄前儿童最常见的慢性疾病。 美国。它已被证明有很大的遗传性，但没有共识的知识。 与ECC相关的遗传变异的存在。现有的全基因组关联研究 ECC很少，并使用龋病的Logistic回归进行单基因关联作图 DMFS/DMFT/DFS/DFT的影响状态或计数回归。这些方法的缺点 包括对年龄效应的潜在错误描述，来自单个SNP的弱信号，以及 多次测试修正。第一个缺点导致基于模型的 关联测试。第二和第三个缺点阻碍了关联映射的威力。 以年龄到ECC或DMFS计数过程为表型的多基因座检验可以解决 第一个和第二个缺点，并缓解第三个缺点。随年龄变化的惩罚变量选择 将ECC作为表型可以解决第一和第三个缺点。然而，年龄到ECC和 在龋病研究中，DMF的计数过程不可避免地会被区间审查，因为 持续监测龋病的影响或严重程度是不可能的。这使得现有的多- 基因座生存检验和高维生存回归不适用于遗传 ECC的关联研究。这个项目的目标是开发新的人口方法- 基于和基于家庭的生存结果的遗传关联分析，它解决了 上述缺点和区间筛选的复杂性，剖析了遗传结构 ECC。具体目标是1)开发一套基于集合的遗传关联和相互作用 生存结果服从区间截尾和可能的左截断的检验，2)到 开发一套基于SET的遗传关联和交互测试， 3)发展了一套区间截尾和区间截尾的高维变量选择方法 可能留下了截断的数据。新方法将被编程到R包中，以 通过全面R档案网传播。此外，我们还将应用 DBGaP数据集的方法：龋齿：全基因组关联和基因x 环境研究(DC-Gage)，以说明其用途并发现主题知识。 该项目的成功完成将解决阻碍ECC的分析挑战 基因研究，推动以人口为基础的统计方法和 一般生存结果的基于家庭的遗传关联分析。的应用程序 DC-Gage数据的新方法将为ECC的遗传病因提供新的见解。

Project Summary Early childhood caries (ECC) is the most common chronic disease in preschool children in the United States. It has been shown to have a substantial heritability, but no consensus knowledge of the genetic variants associated with ECC exists. Existing genome-wide association studies of ECC are few and performed single-locus association mapping using logistic regression of caries affection status or count regression of DMFS/DMFT/DFS/DFT. Drawbacks of those approaches include potential misspecification of age effect, weak signal from single SNP, and the burden of multiple testing correction. The first drawback leads to incorrect sizes for the model-based association tests. The second and third drawbacks hinder the power of the association mapping. Multi-locus tests with age to ECC or the counting process of DMFS as phenotype can address the first and second drawbacks and alleviate the third one. Penalized variable selection with age to ECC as phenotype can address the first and third drawbacks. However, age to ECC and the counting process of DMFS are inevitably interval censored in caries research studies, as continuous monitoring of caries affection or severity is impossible. This makes the existing multi- locus survival tests and high-dimensional survival regressions not applicable to genetic association studies of ECC. The goal of this project is to develop novel methods for population- based and family-based genetic association analyses of survival outcomes, which address the above drawbacks and the interval censoring complexity, to dissect the genetic architecture of ECC. The specific aims are 1) to develop a suite of set-based genetic association and interaction tests with survival outcomes subject to interval censoring and possible left truncation, 2) to develop a suite of set-based genetic association and interaction tests with panel count outcomes, and 3) to develop a set of high-dimensional variable selection methods for interval censored and possibly left truncated data. The new methods will be programmed into R packages to be disseminated through the Comprehensive R Archive Network. Additionally, we will apply the methods to a dbGaP data set, Dental Caries: Whole Genome Association and Gene x Environment Studies (DC-GAGE), to illustrate their utility and discover subject matter knowledge. The successful completion of this project will address analytic challenges that impede ECC genetic research, and advance the statistical methodology development for population-based and family-based genetic association analyses of survival outcomes in general. The application of the new methods to the DC-GAGE data will provide new insights into the genetic etiology of ECC.