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.

项目概要 儿童早期龋齿（ECC）是学龄前儿童最常见的慢性疾病 美国。它已被证明具有显着的遗传力，但尚未达成共识 存在与 ECC 相关的遗传变异。现有的全基因组关联研究 ECC 很少，并使用龋齿的逻辑回归进行单位点关联映射 DMFS/DMFT/DFS/DFT 的影响状态或计数回归。这些方法的缺点 包括年龄效应的潜在错误指定、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尺寸不正确 关联测试。第二个和第三个缺点阻碍了关联映射的威力。 以 ECC 年龄或 DMFS 计数过程作为表型的多位点测试可以解决 第一个和第二个缺点并减轻了第三个缺点。随着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，ECC 的年龄和 DMFS 的计数过程在龋齿研究中不可避免地受到区间删失，如 连续监测龋齿的影响或严重程度是不可能的。这使得现有的多 位点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针对人口的新方法 基于和基于家族的生存结果遗传关联分析，解决了 上述缺点和区间审查的复杂性，来剖析遗传结构 ECC。具体目标是 1）开发一套基于集合的遗传关联和交互 生存结果的测试受到区间审查和可能的左截断，2) 开发一套基于集合的遗传关联和相互作用测试与面板计数结果， 3）开发一套用于区间删失的高维变量选择方法 可能留下被截断的数据。新方法将被编程到 R 包中 通过综合 R 档案网络传播。此外，我们将应用 dbGaP 数据集的方法，龋齿：全基因组关联和 Gene x 环境研究 (DC-GAGE)，用于说明其实用性并发现主题知识。 该项目的成功完成将解决阻碍 ECC 的分析挑战 遗传研究，并推进基于人口和人口的统计方法的开发 一般生存结果的基于家族的遗传关联分析。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。