Methods to enable robust and efficient use of genetic summary data

能够稳健、高效地使用遗传摘要数据的方法

• 批准号: 10462613
• 负责人: Audrey E Hendricks
• 金额: $ 40.8万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462613
• 关键词: Bioconductor; Data; Data Analyses; Data Set; Databases; Disease; Frequencies; Genetic; Genetic Predisposition to Disease; Genetic Research; Genome; Genotype; Health; Heterogeneity; Individual; Internet; Lead; Methods; Modeling; Rare Diseases; Resources; Techniques; Testing; case control; causal variant; data resource; genetic element; genetic variant; genome wide association study; genomic data; innovation; insight; polygenic risk score; precision medicine; statistics

Abstract Publiclyavailable genetic summary data canhave high utility for providing insight into genetic etiology of health and disease. Databases of genotype frequencies, such as the genome Aggregation Database (gnomAD), are used to prioritize putative causal variants and, more recently, as pseudo-controls in case-control analysis. Genome Wide Association Study (GWAS) test statistics are used in a variety of secondary data analyses including polygenic risk scores (PRS), genetic correlation analysis, and fine mapping of causal variants. Compared with individual level data, genetic summary data often has fewer barriers in access, promoting broad use of these valuable data resources. The availability and use of summary genetic data is often not equitable across all ancestral groups, especially for understudied ancestral groups that have little to no representation within these resources. Furthermore, heterogeneity within the summary data can lead to confounding and reduced power for case-control analysis, incorrect prioritization of putative causal variants for rare diseases, and reduced accuracy for polygenic risk scores. I develop robust and efficient methods to appropriately use genetic summary data while estimating, modeling, and harnessing the heterogeneity within. My methods coalesce around a unifying framework where I flip the paradigm of genetic and genomic data treating the genetic variant or element as the observational unit by which we analyze the data rather than the individual. This simple, yet innovative paradigm shift enables the use of classical statistical techniques and the creation of methods that detect, adjust for, and even use heterogeneity within summary level data. To enable broad and equitable use of our methods, we will create publicly available R packages compatible with Bioconductor and Shiny Apps for interactive internet use.

摘要 公开可用的遗传总结数据对于提供遗传病因学见解具有很高的实用性 健康和疾病的关系。基因型频率数据库，如基因组聚合数据库 （gnomAD），用于优先考虑假定的因果变异，最近，作为伪对照， 病例对照分析全基因组关联研究（GWAS）检验统计量用于各种 二次数据分析，包括多基因风险评分（PRS），遗传相关性分析和精细 因果变量的映射。与个体水平的数据相比，遗传学概要数据往往较少 在获取方面的障碍，促进这些宝贵的数据资源的广泛使用。的供应和使用 摘要遗传数据在所有祖先群体中往往是不公平的，特别是对于研究不足的群体， 在这些资源中几乎没有代表性的祖先群体。此外，委员会认为， 汇总数据中的异质性可导致混杂和病例对照功效降低 分析，罕见疾病的假定因果变异的优先顺序不正确， 多基因风险评分。我开发了强大而有效的方法来适当地使用遗传总结 数据，同时估计、建模和利用内部的异质性。我的方法结合了 一个统一的框架，我翻转了遗传和基因组数据的范式， 或者元素作为我们分析数据的观察单位，而不是个人。这 简单，但创新的范式转变，使使用经典的统计技术和创造 检测、调整甚至使用汇总级数据中的异质性的方法。使 广泛和公平地使用我们的方法，我们将创建公开可用的R软件包， Bioconductor和闪亮的应用程序用于交互式互联网使用。