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 母基金的最终目标是开发严格、高效和健壮的综合建模 通过利用大量的公共可用的 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。父母补助金有五个具体目标，前三个目标 为跨人群风险预测制定三种互补办法，包括：（目标1）a 贝叶斯方法（ME-Pred），沿着我们发表的工作路线，将功能注释 信息或多个性状信息，其明确地模拟来自多个群体的联合效应大小， 功能注释;（目标2）经验贝叶斯方法（GWEB），认为更一般， 灵活的效应量分布和统计推断，不需要验证队列来调整一些 模型参数;和（目标3）一个快速和强大的贝叶斯非参数方法（SDPR），是高度 适应不同的遗传结构，并且计算效率高。父母补助金的目标4和目标5 重点是实施和应用开发的工具，以一些研究，包括 由耶鲁医学院和耶鲁纽黑文卫生系统联合发起的世代项目。 我们已经组建了一个研究团队，他们具有统计遗传学，医学遗传学和高- 性能计算，以开发，实施，评估和传播所提出的方法。为此 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 由家长资助。此外，我们还将考虑我们的方法在评估疾病方面的应用 风险的参与者在我们所有的研究计划与大量的代表性不足 个体，其中为混合受试者定制的方法可以提供显著改进的预测。这 补充剂不仅提供了一个极好的训练机会，而且还将为疾病开发新的工具， 风险预测。