Efficient methods for genome-wide survival analysis of early childhood caries

儿童早期龋齿全基因组生存分析的有效方法

• 批准号: 10696112
• 负责人: Chenxi Li
• 金额: $ 15.08万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10696112
• 关键词: Address; Age; Age related macular degeneration; Archives; Area; Caries prevention; Child; Chronic Disease; Complex; Consensus; Data; Data Set; Dental caries; Dentition; Development; Disease; Early identification; Economically Deprived Population; Environment; Etiology; Event; Family; GAGE; Genes; Genetic; Genetic Heterogeneity; Genetic Predisposition to Disease; Genetic Research; Genetic Risk; Genetic study; Goals; Heritability; Individual; Knowledge; Life Cycle Stages; Logistic Regressions; Mammary Neoplasms; Maps; Methodology; Methods; Modeling; Monitor; Names; Nursery Schools; Outcome; Pathway interactions; Phenotype; Prevention; Primary Dentition; Process; Public Health; Recurrence; Research; School-Age Population; Severities; Signal Transduction; Statistical Methods; Survival Analysis; Testing; Time; Tooth structure; Twin Studies; United States; Variant; affection; age effect; analytical tool; database of Genotypes and Phenotypes; early childhood; experience; genetic architecture; genetic association; genetic epidemiology; genetic variant; genome wide association study; genome-wide; high dimensionality; improved; insight; method development; next generation sequencing; novel; permanent tooth; phenotypic data; population based; risk variant; socioeconomic disadvantage; statistics; success; survival outcome; tooth surface; trait; whole genome

Project Summary Early childhood caries (ECC) is the most common chronic disease in preschool-age children in the United States. It has been shown to have a substantial heritability, but no consensus exists regarding ECC-associated genetic risk loci. Existing genome-wide association studies (GWAS) of ECC are scarce and were based on single-locus association mapping using logistic regression of binary traits (e.g., caries affection status) or count regression of quantitative traits (e.g., dmfs/dmft/dfs/dft). Drawbacks of those approaches include potential misspecification of the age effect, not making full use of tooth-/tooth-surface-level caries lifetime data, potentially weak signals from individual variants, and the burden of multiple testing correction. The first drawback may lead to incorrect type I error rates from model-based association tests. The others hinder statistical power. Multi-locus tests with tooth-/tooth-surface-level ages to caries or the counting process of dmfs/dmft/dfs/dft as phenotypes can address the above drawbacks. However, caries life course data are inevitably interval censored since continuous monitoring of caries affection or severity is impractical in caries research. No existing multi-locus survival tests can apply to tooth- /tooth-surface-level times to caries or the counting process of dmfs/dmft/dfs/dft subject to interval censoring. The goal of this project is to develop two suites of 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 two suites of set-based genetic association tests respectively for multivariate interval-censored survival outcomes and panel count outcomes and 2) to apply the methods to two large-scale real-world data sets on ECC, Dental Caries: Whole Genome Association and Gene x Environment Studies and ZOE 2.0, to illustrate the utility of the methods and discover subject matter knowledge. Additionally, the new methods will be programmed into R packages to be disseminated through the Comprehensive R Archive Network. 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 real 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 关于 ECC 相关遗传风险位点。现有全基因组关联研究（GWAS） ECC 的数量很少，并且基于使用逻辑回归的单基因座关联映射 二元性状（例如，龋齿患病状况）或数量性状的计数回归（例如， dmfs/dmft/dfs/dft)。这些方法的缺点包括可能错误指定年龄 效果，未充分利用牙齿/牙齿表面水平的龋齿寿命数据，可能较弱 来自各个变体的信号，以及多重测试校正的负担。第一个缺点 可能会导致基于模型的关联测试产生不正确的 I 类错误率。其他人阻碍 统计功效。多位点测试，包括牙齿/牙齿表面水平的龋齿年龄或计数 dmfs/dmft/dfs/dft 作为表型的过程可以解决上述缺点。然而，龋齿 由于持续监测龋齿影响或情况，生命历程数据不可避免地会受到间隔审查 严重程度在龋齿研究中是不切实际的。现有的多位点生存测试不能应用于牙齿 /牙面龋齿次数或dmfs/dmft/dfs/dft间隔时间的计数过程 审查。该项目的目标是开发两套方法，用于基于人口和 基于家族的生存结果遗传关联分析，解决上述问题 缺点和区间审查的复杂性，剖析 ECC 的遗传结构。这 具体目标是 1) 分别开发两套基于集合的遗传关联测试 多变量区间删失生存结果和小组计数结果，2) 应用 ECC、龋齿：全基因组上两个大规模现实世界数据集的方法 关联和基因 x 环境研究和 ZOE 2.0，以说明方法的实用性 并发现主题知识。此外，新方法将被编程到 R 软件包将通过综合 R 档案网络传播。成功者 该项目的完成将解决阻碍 ECC 遗传研究的分析挑战，以及 推进基于人群和基于家庭的遗传统计方法的开发 一般生存结果的关联分析。新方法的应用 真实数据将为 ECC 的遗传病因学提供新的见解。