Survival genetics methods for genetic association studies of early childhood caries

用于早期儿童龋齿遗传关联研究的生存遗传学方法

• 批准号: 10453481
• 负责人: Chenxi Li
• 金额: $ 31.2万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-12 至 2023-08-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10453481
• 关键词: Address; Age; Alzheimer' s Disease; Archives; Area; Caries prevention; Chronic Disease; Complex; Consensus; Data; Data Set; Dental caries; Development; Disease; Environment; Etiology; Event; Family; GAGE; Genes; Genetic; Genetic Diseases; Genetic Predisposition to Disease; Genetic Research; Genetic study; Goals; Heritability; Knowledge; Left; Literature; Logistic Regressions; Measures; Menopause; Methodology; Methods; Modeling; Monitor; Non-Insulin-Dependent Diabetes Mellitus; Osteoporosis; Outcome; Phenotype; Preschool Child; Prevention; Primary Dentition; Process; Property; Public Health; Research; Severities; Signal Transduction; Statistical Methods; Survival Analysis; Testing; Time; Twin Studies; United States; Woman; affection; age effect; analytical tool; base; database of Genotypes and Phenotypes; disadvantaged population; early childhood; experience; genetic architecture; genetic association; genetic epidemiology; genetic variant; genome wide association study; genome-wide; high dimensionality; insight; method development; next generation sequencing; novel; permanent tooth; population based; research study; semiparametric; socioeconomic disadvantage; statistics; success; survival outcome; whole genome

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.

项目摘要 幼儿龋病是我国学龄前儿童最常见的慢性疾病， 美国的它已被证明有很大的遗传性，但没有共识的知识 与ECC相关的遗传变异存在。现有的全基因组关联研究 ECC很少，用Logistic回归进行单位点关联作图 DMFS/DMFT/DFS/DFT的患病状况或计数回归。这些方法的缺点 包括年龄效应的潜在错误指定、来自单个SNP的弱信号以及 多重检验校正第一个缺点导致基于模型的 联想测试第二和第三个缺点阻碍了关联映射的能力。 多位点检测以年龄为ECC或DMFS的计数过程作为表型可以解决 第一个和第二个缺点并减轻第三个缺点。随年龄的惩罚变量选择 以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，DMFS的计数过程不可避免地是区间删失的， 不可能连续监测龋齿的影响或严重性。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。该项目的目标是开发新的人口方法- 基于和基于家族的生存结局遗传关联分析， 上述缺点和区间删失的复杂性，剖析遗传结构， 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模型 生存结局检验服从区间删失和可能的左截断，2）至 开发一套基于集合的遗传关联和相互作用测试，具有面板计数结果， 3）提出了一套区间删失的高维变量选择方法， 可能留下截断的数据。新方法将被编程到R包中， 通过全面的R档案网络传播。此外，我们将应用 方法的dbGaP数据集，龋齿：全基因组协会和基因x 环境研究（DC-GAGE），以说明其效用和发现主题知识。 该项目的成功完成将解决阻碍ECC的分析挑战 遗传研究，并推进基于人口的统计方法的发展， 基于家族的生存结果的遗传关联分析。的应用 DC-GAGE数据的新方法将为ECC的遗传病因学提供新的见解。