Novel methods to improve the utility of genomics summary statistics

自2000年人类基因组测序以来的二十年里， 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。 基因组学数据成本的降低意味着研究人员现在可以获得完整的基因组 数十万人的序列信息，通过大规模的 电子健康记录（EHR）和生物库的存储库。然而，相关的计算，统计 如何利用这些数据来研究遗传变异的贡献， 常见的疾病。重要的是，有必要进行方法创新，以尽量减少计算量， 复杂性和尊重数据隐私问题，同时最大限度地提高数据访问和效用。的最前沿 这些创新是利用非个体可识别的汇总统计的计算方法， 基于生物库/EHR数据进行计算，以最大限度地提高下游功能理解和临床实用性。一 一组新兴的汇总统计量是来自独立回归模型的点估计值和标准误差 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 主成分（PC）。任何一组预先计算的汇总统计量的一个关键限制是， 能够预测这些统计数据的所有可能的下游用途。例如，研究人员可能希望用途： (a)与预先计算的分析中考虑的协变量不同的协变量集，（B）遗传变异集， 预测因子，而不是单一的标记物和（c）替代表型定义，是现有的功能， 变量（例如，一位研究人员想了解一种具有临床重要性的表型，但只有前-𝑌𝑌𝐶𝐶 计算的汇总统计量，其中=（ 1， 2，.，））。𝑓𝑓𝑌𝑌𝐶𝐶𝑌𝑌𝑘𝑘𝑌𝑌在这个项目中，我们将（1）开发一个 评估具有临床相关表型的遗传变异的计算有效框架， 汇总统计，并应用这些方法对临床相关表型进行协调分析 在多队列研究中使用汇总统计量，（2）验证这些方法的可行性 两个相关财团的创新，目前正在探索遗传变异对认知结果的作用， 膳食多不饱和脂肪酸（PUFA）水平的潜在调节和/或介导作用。初步 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 在广泛的临床相关表型的模拟和真实的数据上。工作将为 为未来的R 01项目提供额外的方法扩展，更广泛的测试， 全面传播。

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.