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.

自测序以来的二十年里，实验和计算上不断取得突破 人类基因组为了解人类疾病的病因学提供了前所未有的机会。 基因组学数据成本的下降意味着研究人员现在可以获得完整的基因组 数十万个人的序列信息，可以通过大型数据库广泛访问这些数据 然而，电子健康记录（EHR）和生物样本库是相互关联的计算、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 重要的是，需要方法创新来最大限度地减少计算。 复杂性并尊重数据隐私问题，同时最大限度地提高数据访问和实用性。 这些创新是利用非个人可识别的汇总统计数据进行计算的方法。 根据生物样本库/电子病历数据进行计算，以最大限度地提高下游功能理解和临床效用。 新兴的汇总统计数据集是来自单独回归模型的点估计和标准误差 表型 (Y) 对个体基因型 (Xi) 的影响，有时需要有限的协变量调整（例如年龄、性别） 任何一组预先计算的汇总统计数据的一个关键限制是不被计算。 能够预测此类统计数据的所有可能的下游用途，例如，研究人员可能希望使用： (a) 与预先计算分析中考虑的协变量不同的协变量组，(b) 遗传变异组 预测变量，而不是单个标记，以及（c）作为现有功能的替代表型定义 变量（例如，研究人员想了解临床上重要的表型，𝑌𝑌𝐶𝐶，但只有预 计算 𝑌𝑌1, 𝑌𝑌2, … , 𝑌𝑌𝑘𝑘 的汇总统计数据，其中 𝑌𝑌𝐶𝐶 = 𝑓𝑓( 𝑌𝑌1, 𝑌𝑌2, … , 𝑌𝑌𝑘𝑘））））在这个项目中，我们将（1）开发一个 使用计算有效的框架来评估具有临床相关表型的遗传变异 汇总统计数据并应用这些方法对临床相关表型进行统一分析 在多队列研究中使用汇总统计数据并（2）验证这些方法的可行性 两个相关联盟的创新目前正在探索遗传变异对认知结果的作用和 膳食多不饱和脂肪酸（PUFA）水平的潜在调节和/或调节作用。 方法将在开源工具（R/python 包）中实现，并且还将涉及广泛的测试 基于广泛的临床相关表型的模拟和真实数据的工作将为我们奠定基础。 为未来的 R01 项目提供额外的方法扩展、更广泛的测试和 全面传播。