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.

项目概要 阿尔茨海默氏病（AD）是一种进行性神经退行性疾病，受遗传和遗传因素影响 和环境因素。尽管超过 50 个具有全基因组意义的风险位点已被 迄今为止，AD 遗传性的很大一部分仍然无法解释。随着高 随着通量技术的发展，大量的遗传数据已可用于 AD 遗传 研究。研究利用这些丰富的数据资源并考虑性别依赖性 遗传效应、多个标记物的联合效应以及 AD 风险信息（例如 AD 发生时间） 表型）为新的 AD 基因发现提供了巨大的希望，为此提供了严格的分析工具 仍缺乏分析。大多数统计工具无法解释遗传异质性。 此外，现有的多标志物生存测试很大程度上基于协变量的 Cox 模型 调整。错误指定协变量调整模型可能会导致虚假关联 发现。此外，AD 的时间通常在队列研究中进行区间删失，并受 竞争死亡风险，但目前没有多标志物生存测试可用于处理间隔 审查竞争风险数据。解决现有方法的局限性并促进 考虑性别相关遗传的 AD 时间结果的遗传关联分析 异质性，我们将根据附加危险开发三种多标记生存测试 分别是模型、加速失效时间模型和区间删失生存特征。我们 将进一步扩展这三个测试以进行基因-基因/基因-环境相互作用分析。所有的 新的测试可以处理左截断和竞争风险，这是 AD 时间中的两个常见问题 分析。新方法将被编程到 R 包中，并通过 综合 R 档案网络。此外，我们将把这些方法应用到英国生物银行 ROSMAP 数据用于搜索 AD 相关基因并测试基因-性别相互作用。这 该项目的成功完成将解决正在进行的AD所面临的分析挑战 遗传学研究，推进遗传关联统计方法的发展 一般生存结果分析。新方法在英国生物样本库的应用 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。