Survival genetics methods for detecting sex-dependent genetic effects on Alzheimer’s disease

用于检测阿尔茨海默病性别依赖性遗传效应的生存遗传学方法

• 批准号: 10670493
• 负责人: Chenxi Li
• 金额: $ 38.45万
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10670493
• 关键词: Accounting; Address; Age; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease risk; Archives; Cohort Studies; Communities; Cox Models; Data; Data Analyses; Development; Disease; Disease Outcome; Environmental Risk Factor; Etiology; Failure; Genes; Genetic; Genetic Heterogeneity; Genetic Predisposition to Disease; Genetic Research; Genetic study; Hazard Models; Heritability; Heterogeneity; Individual; Joints; Knowledge; Late Onset Alzheimer Disease; Lead; Left; Literature; Measures; Methodology; Methods; Modeling; Molecular; Neurodegenerative Disorders; Phenotype; Price; Publishing; Research; Risk; Signal Transduction; Specific qualifier value; Statistical Methods; Study Subject; Subgroup; Survival Analysis; Testing; Time; Twin Studies; Variant; affection; analytical tool; base; biobank; cohort; community based participatory research; data resource; disease phenotype; epigenetic marker; gene discovery; gene environment interaction; gene interaction; genetic architecture; genetic association; genetic testing; genetic variant; genome-wide; high throughput technology; human disease; improved; insight; interest; mortality risk; novel; research study; risk variant; sex; success; survival outcome; tool; trait; transcriptomics; user-friendly

Project Summary Alzheimer's disease (AD) is a progressive neurodegenerative disease influenced by both genetic and environmental factors. Although over 50 risk loci with genome-wide significance have been identified to date, a substantial proportion of AD heritability remains unexplained. With the high- throughput technologies, a large amount of genetic data has become available for AD genetic research. While studies utilizing these enriched data resources and considering sex-dependent genetic effects, joint effects of multiple markers, and AD risk information (e.g., time-to-AD phenotype) hold great promise for novel AD gene discovery, rigorous analytical tools for such analysis are still lacking. Most of the statistical tools can't account for genetic heterogeneity. Besides, existing multi-marker survival tests are largely based on the Cox model for covariate adjustment. Mis-specifying the covariate-adjustment model could lead to spurious association findings. Furthermore, time to AD is usually interval censored in cohort studies and subject to the competing risk of death, but no multi-marker survival test is currently available to handle interval censored competing risks data. To address the limitations of existing methods and facilitate genetic association analysis of time-to-AD outcomes considering sex-related genetic heterogeneity, we will develop three multi-marker survival tests based on the additive hazards model, the accelerated failure time model, and interval censored survival traits, respectively. We will further extend these three tests for gene-gene/gene-environment interaction analyses. All the new tests can deal with left truncation and competing risks, two common issues in time-to-AD analyses. 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 the UK Biobank and ROSMAP data to search for AD-associated genes and test for gene-sex interactions. The successful completion of this project will address analytic challenges faced by the ongoing AD genetic research, and advance the statistical methodology development for genetic association analysis of survival outcomes in general. The application of the new methods to the UK Biobank and ROSMAP data will provide new insights into the genetic architecture of AD, especially the sex-specific genetic etiology.

项目摘要 阿尔茨海默氏病（AD）是一种受遗传学影响的进行性神经退行性疾病 和环境因素。尽管超过50个具有全基因组意义的风险基因座是 迄今为止确定的AD遗传力仍然无法解释。高 - 吞吐量技术，大量遗传数据已用于AD遗传 研究。利用这些丰富的数据资源并考虑性依赖性的研究 遗传效应，多个标记的关节效应和AD风险信息（例如，AD的时间 表型）对新型广告基因发现，严格的分析工具具有巨大的希望 分析仍然缺乏。大多数统计工具无法解释遗传异质性。 此外，现有的多标记生存测试主要基于Cox模型的协变量 调整。错误指定的协变量调整模型可能导致虚假关联 发现。此外，通常在队列研究中审查AD的时间并受到 竞争死亡风险，但目前尚无多标记生存测试来处理间隔 审查竞争风险数据。解决现有方法的局限性并促进 考虑与性别相关的遗传的遗传结合分析 异质性，我们将根据添加剂危害开发三个多标记生存测试 模型，加速故障时间模型和间隔审查的生存特征。我们 将进一步扩展这三个测试，以进行基因/基因 - 环境相互作用分析。全部 新测试可以处理左截断和竞争风险，这是两个常见的问题 分析。新方法将被编程成r套件，以通过 全面的R档案网络。此外，我们将把方法应用于英国生物库 和Rosmap数据以搜索与AD相关的基因并测试基因性相互作用。这 该项目的成功完成将解决正在进行的广告所面临的分析挑战 遗传研究并推进遗传关联的统计方法发展 一般生存结果分析。新方法将新方法应用于英国生物库 Rosmap数据将为AD的遗传结构提供新的见解，尤其是 性别特异性遗传病因。