Statistical Methods for Genetic Risk Predictions across Diverse Populations

不同人群遗传风险预测的统计方法

• 批准号: 10731582
• 负责人: HONGYU ZHAO
• 金额: $ 8.39万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-08 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731582
• 关键词: All of Us Research Program; Bayesian Method; Clinical; Complex; Data; Disease; Generations; Genetic; Genetic Risk; Goals; Health system; High Performance Computing; Individual; Joints; Medical Genetics; Methods; Modeling; Participant; Population; Population Heterogeneity; Publishing; Research Personnel; Statistical Methods; Translations; Underrepresented Populations; Validation; Work; cohort; computerized tools; disorder risk; flexibility; genetic architecture; genome wide association study; improved; medical schools; novel; parent grant; risk prediction; risk prediction model; tool; training opportunity; trait

Project Summary The ultimate goal of the parent grant is to develop rigorous, efficient, and robust integrative modeling approaches for risk prediction across populations by capitalizing on the vast amount of publicly available GWAS summary data, abundant functional annotations, and a growing number of studies with participants from underrepresented populations. There are five specific aims in the parent grant, with the first three aims developing three complementary approaches for cross-population risk predictions, including: (Aim 1) a Bayesian approach (ME-Pred), along the line of our published work to incorporate either functional annotation information or multiple trait information, that explicitly models joint effect sizes from multiple populations and functional annotations; (Aim 2) an empirical Bayes approach (GWEB) that considers a more general and flexible effect size distribution and statistical inference that does not need a validation cohort for tuning some model parameters; and (Aim 3) a fast and robust Bayesian nonparametric method (SDPR) that is highly adaptive to different genetic architectures and is computationally efficient. Aim 4 and Aim 5 of the parent grant focus on implementation and applications of the developed tools to a number of studies, including the Generations Project jointly initiated by the Yale School of Medicine and the Yale New Haven Health System. We have assembled a team of investigators with expertise in statistical genetics, medical genetics, and high- performance computing to develop, implement, evaluate, and disseminate the proposed methods. For this diversity supplement project, we will consider risk prediction in admixed individuals, a topic related to but covered by the parent grant. In addition, we will consider the applications of our methods to evaluate disease risk for participants in the All of Us Research Program with a substantial number of underrepresented individuals where methods tailored for admixed subjects may provide significantly improved predictions. This supplement will not only provide an excellent training opportunity, but will also develop new tools for disease risk predictions.

项目概要 家长资助的最终目标是开发严格、高效、稳健的集成模型 通过利用大量公开信息来预测跨人群风险的方法 GWAS汇总数据、丰富的功能注释以及越来越多的参与者研究 来自代表性不足的人群。家长补助金有五个具体目标，其中前三个目标 开发三种互补的跨人群风险预测方法，包括：（目标 1）a 贝叶斯方法 (ME-Pred)，沿着我们已发表的作品的思路，合并任一功能注释 信息或多性状信息，明确模拟来自多个群体的联合效应大小，并且 功能注释； （目标 2）一种经验贝叶斯方法（GWEB），它考虑了更一般和 灵活的效应大小分布和统计推断，不需要验证队列来调整某些 模型参数； (目标 3) 一种快速且稳健的贝叶斯非参数方法 (SDPR) 适应不同的遗传结构并且计算效率高。家长补助金的目标 4 和目标 5 重点关注已开发工具在多项研究中的实施和应用，包括 世代项目由耶鲁大学医学院和耶鲁纽黑文卫生系统联合发起。 我们组建了一支在统计遗传学、医学遗传学和高科技领域拥有专业知识的研究人员团队。 性能计算来开发、实施、评估和传播所提出的方法。为了这 多样性补充项目，我们将考虑混合个体的风险预测，这是一个与但相关的主题 由家长补助金覆盖。此外，我们将考虑应用我们的方法来评估疾病 “我们所有人研究计划”的参与者面临着大量代表性不足的风险 为混合受试者量身定制的方法可能会提供显着改进的预测。这 补充剂不仅将提供绝佳的培训机会，还将开发针对疾病的新工具 风险预测。