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; 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 幼儿时期龋齿（ECC）是学龄前儿童中最常见的慢性疾病 美国。已显示出具有实质性的遗传力，但没有共识知识 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，并且使用龋齿的逻辑回归进行了单位核心关联映射 DMFS/DMFT/DFS/DFT的感情状态或计数回归。这些方法的缺点 包括年龄效应的潜在错误指定，单个SNP的信号较弱以及负担 多次测试校正。第一个缺点导致基于模型的大小不正确 协会测试。第二和第三个缺点阻碍了关联映射的力量。 随着年龄至ECC或DMFS的计数过程，多级稳定的测试作为表型可以解决 第一个和第二个缺点，减轻了第三个缺点。随着年龄的增长而惩罚的变量选择 作为表型可以解决第一个和第三个缺点。但是，ECC的年龄和 DMF的计数过程不可避免地在龋齿研究中审查，作为 连续监测龋齿的感情或严重程度是不可能的。这使现有的多 基因座生存测试和不适用于遗传的高维生存回归 ECC协会研究。该项目的目的是为人口开发新的方法 - 基于家庭和基于家庭的遗传关联分析生存结果，以解决 上述缺点和间隔检查复杂性，以剖析 ECC。具体目的是1）开发基于集合的遗传关联和相互作用的套件 具有间隔检查和可能的左截断的生存结果的测试，2） 开发一套基于集合的遗传关联和与面板计数结果的相互作用测试， 3）为间隔审查和 可能是左截短的数据。新方法将被编程到r套件中 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：整个基因组关联和基因X 环境研究（DC-GAGE），以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，并推动基于人群和的统计方法发展 一般来说，基于家庭的遗传关联分析。应用 DC-GAGE数据的新方法将为ECC的遗传病因提供新的见解。