Novel methods to improve the utility of genomics summary statistics

提高基因组学汇总统计效用的新方法

• 批准号: 10646125
• 负责人: Nathan L Tintle
• 金额: $ 41.22万
• 依托单位: FATTY ACID RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646125
• 关键词: Acceleration; Address; Age; Aging; Alzheimer' s Disease; Clinical; Cognitive; Cohort Studies; Computer software; Computing Methodologies; Confidentiality of Patient Information; Data; Data Science; Data Set; Databases; Development; Disease; Documentation; Educational workshop; Electronic Health Record; Ensure; Etiology; Fatty Acids; Funding; Future; Genetic; Genetic Markers; Genetic Variation; Genetic study; Genome; Genomics; Genotype; Health; Heart; Human; Impaired cognition; Individual; Linear Models; Mediating; Mediation; Meta-Analysis; Methodology; Methods; Modeling; National Human Genome Research Institute; Outcome; Outcomes Research; Phenotype; Polyunsaturated Fatty Acids; Privacy; Pythons; Research; Research Personnel; Role; Statistical Methods; Testing; Training; Validation; Work; biobank; clinically relevant; cohort; cost; data access; data privacy; dietary; digital repositories; flexibility; genetic epidemiology; genetic variant; genome sciences; genomic data; human disease; human genome sequencing; improved; innovation; interest; member; novel; open source; open source tool; response; sex; statistics; survival outcome; tool; working group

The repeated experimental and computational breakthroughs in the two decades since the sequencing of the human genome have provided an unprecedented opportunity to understand the etiology of human diseases. The diminishing cost of genomics data means it is now possible for researchers to obtain complete genome sequence information on hundreds of thousands of individuals, with widespread access to those data via large repositories of electronic health records (EHRs) and biobanks. However, interrelated computational, statistical and privacy questions remain for about how to leverage these data to study the contribution of genetic variation to common diseases. Importantly, there is a need for methodological innovation to minimize computational complexity and respect data privacy concerns while maximizing data access and utility. At the forefront of these innovations are computational methods that leverage non-individually identifiable summary statistics pre- computed on biobank/EHR data to maximize downstream functional understanding and clinical utility. One emerging set of summary statistics are point estimates and standard errors from separate regression models of a phenotype (Y) on individual genotypes (Xi), sometimes with limited covariate adjustment (e.g., Age, Sex and principal components (PCs)). A key limitation of any set of pre-computed summary statistics is not being able to anticipate all possible downstream uses of such statistics. For example, researchers may want to use: (a) different sets of covariates than those considered in pre-computed analyses, (b) sets of genetic variants as predictors, instead of single markers and (c) alternative phenotype definitions that are functions of existing variables (e.g., a researcher want to know about a phenotype, 𝑌𝑌𝐶𝐶, of clinical importance, but only has pre- computed summary statistics on 𝑌𝑌1, 𝑌𝑌2, … , 𝑌𝑌𝑘𝑘, where 𝑌𝑌𝐶𝐶 = 𝑓𝑓( 𝑌𝑌1, 𝑌𝑌2, … , 𝑌𝑌𝑘𝑘)). In this project we will (1) develop a computationally efficient framework to evaluate genetic variants with clinically relevant phenotypes using summary statistics and apply these methods to perform harmonized analyses of clinically relevant phenotypes in multi-cohort studies using summary statistics and (2) validate the feasibility of these methodological innovations within two related consortia currently exploring the role genetic variants on cognitive outcomes and the potential moderating and/or mediating role of dietary polyunsaturated fatty acid (PUFA) levels. Preliminary methods will be implemented in open-source tools (R/python packages), and will also involve extensive testing on both simulated and real data across a wide range of clinically relevant phenotypes. Work will set the stage for a future R01 project to provide additional methodological expansion, more widespread testing and comprehensive dissemination.

自从测序以来的二十年中，重复的实验和计算突破 人类基因组为了解人类疾病的病因提供了前所未有的机会。 基因组学数据的成本降低意味着现在研究人员可以获得完整的基因组 关于数十万个人的序列信息，可以通过大型访问这些数据 电子健康记录（EHRS）和生物库的存储库。但是，相互关联的计算，统计 关于如何利用这些数据来研究遗传变异的贡献，仍然存在隐私问题 常见疾病。重要的是，有必要创新以最大程度地减少计算 复杂性和尊重数据隐私问题，同时最大化数据访问和实用程序。在 这些创新是计算方法，可利用非个人识别的摘要统计数据。 在生物库/EHR数据上计算以最大程度地提高下游功能理解和临床实用性。一 摘要统计数据的新兴集合是点估计值和来自单独回归模型的标准错误 在单个基因型（XI）上的表型（Y）的，有时具有有限的协变量调整（例如，年龄，性别 和主要组件（PC））。任何一组预计的摘要统计数据的关键限制都不是 它可以预测此类统计数据的所有可能下游用途。例如，研究人员可能想使用： （a）与预先计算的分析中考虑的一组协变量集，（b）遗传变体集作为 预测因素，而不是单个标记和（c）是现有功能的替代表型定义 变量（例如，研究人员想了解临床重要性的表型，但仅具有前 计算出𝑌𝑌1，𝑌𝑌2，…，𝑌𝑘𝑘的摘要统计信息，其中𝑌𝑌𝐶𝐶=𝑓𝑓（𝑌𝑌1，𝑌𝑌2，…，𝑌𝑌𝑘𝑘））。在这个项目中，我们将（1）开发一个 使用临床相关表型评估遗传变异的计算有效框架 摘要统计数据，并应用这些方法来进行临床相关表型的统一分析 在使用摘要统计数据和（2）验证这些方法学的可行性的多核研究研究中 目前有两个相关宪法的创新探讨了认知结果和遗传变异的作用 饮食多不饱和脂肪酸（PUFA）水平的潜在调节和/或中介作用。初步的 方法将在开源工具（R/Python软件包）中实施，也将涉及广泛的测试 在广泛的临床相关表型中，模拟和真实数据均可在模拟和实际数据上。工作将设定舞台 对于未来的R01项目，可以提供额外的方法论扩展，更多的宽度测试和 全面传播。