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.

项目摘要 父母赠款的最终目标是开发严格，高效和健壮的整合建模 通过大量公开可用来利用大量人口的风险预测方法 GWAS摘要数据，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。父母赠款中有五个具体目标，前三个目标 开发三种互补方法来进行跨种群的风险预测，包括：（AIM 1）A 贝叶斯方法（ME-PRED），沿我们发表的工作，以结合任何功能注释 信息或多个特征信息，这些信息明确地模拟了来自多个人群的联合效应大小， 功能注释； （AIM 2）一种经验贝叶斯方法（GWEB），该方法考虑了一个更一般的和 柔性效果尺寸分布和统计推断不需要验证队列来调整一些 模型参数； （AIM 3）一种高度高度的贝叶斯非参数方法（SDPR） 适应不同的遗传体系结构，并且在计算上是有效的。目标4并瞄准父母赠款的5 专注于开发工具的实施和应用，包括 几代项目由耶鲁大学医学院和耶鲁纽黑文卫生系统共同发起。 我们已经组建了一个研究人员，在统计遗传学，医学遗传学和高级方面拥有专业知识 绩效计算以开发，实施，评估和传播提出的方法。为了这 多样性补充项目，我们将考虑在混合个人中的风险预测，这是与 由父母赠款覆盖。此外，我们将考虑我们方法评估疾病的应用 我们所有人的参与者的风险，大量代表性不足 为混合受试者量身定制的方法的个体可能会提供明显改善的预测。这 补充剂不仅将提供出色的培训机会，而且还将开发新的疾病工具 风险预测。