Statistical Methods for Genetic Risk Predictions across Diverse Populations

总结 尽管全基因组关联研究（GWAS）在识别遗传变异方面非常成功， 与复杂的疾病和特征相关，将GWAS结果转化为临床应用仍然具有挑战性。 用于改进疾病预测、预防、诊断、预后、监测的有用疾病风险模型， 治疗此外，迄今为止进行的大多数GWAS都集中在欧洲血统的个体上， 这使得很难在其他人群中推导出风险模型。最近的研究表明， 对人群中复杂疾病的贡献以及考虑功能性疾病的潜在益处 交叉人群分析中的注释。该项目的最终目标是开发严谨、高效、 和强大的综合建模方法，通过利用大量的 大量公开可用的GWAS摘要数据，丰富的功能注释，以及越来越多的 参与者来自代表性不足的人群。这将通过五个具体的 目标。前三个目标将为跨人群风险预测开发三种互补方法， 包括：（目标1）贝叶斯方法（ME-Pred），沿着我们发表的工作路线， 功能注释信息或多个特质信息，其显式地从 多个群体和功能注释;（目标2）经验贝叶斯方法（GWEB），认为 更一般和灵活的效应量分布和统计推断，不需要验证队列 用于调整一些模型参数;以及（目标3）快速和鲁棒的贝叶斯非参数方法（SDPR）， 高度适应不同的遗传结构，并且计算效率高。广泛的模拟将 比较这些方法和其他现有方法的性能。在目标4中，我们 应用这些方法来评估各种疾病和特征的潜在临床效用，重点是 代表性不足的人群。我们还将与耶鲁世代项目的调查人员密切合作 研究这些工具对研究参与者的潜在好处，包括许多代表性不足的人 人口。然后，我们将改进一些方法的实现，以减少计算时间， 改进目标5中的用户界面和分析管道。我们已经召集了一个调查小组 统计遗传学，医学遗传学和高性能计算方面的专业知识，以开发，实施， 评估和传播所提出的方法。如果成功，这些方法和工具将导致更多的 在代表性不足的人群中进行准确的遗传风险预测，解决减少健康风险的关键需求 差距

Summary Although genome-wide association studies (GWAS) have been very successful in identifying genetic variants associated with complex diseases and traits, it is still challenging to translate GWAS results into clinically useful disease risk models for improved disease prediction, prevention, diagnosis, prognosis, monitoring, and treatment. Furthermore, most GWAS conducted to date have focused on individuals of European ancestry, making it difficult to derive risk models in other populations. Recent research has suggested shared genetic contributions to complex diseases across populations and the potential benefit of considering functional annotations in cross-population analysis. The ultimate objective of this project 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. This will be accomplished through five specific aims. The first three aims will develop 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. Extensive simulations will be performed to compare the performance of these methods and other existing methods. In Aim 4, we will apply these methods to evaluate the potential clinical utility for various diseases and traits, with a focus on underrepresented populations. We will also work closely with investigators from the Yale Generations Project to study the potential benefit of these tools for its study participants, including many from the underrepresented populations. We will then refine the implementations of some methods to reduce computational time and improve the user interface and analysis pipeline in Aim 5. 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. If successful, these methods and tools will lead to more accurate genetic risk predictions in underrepresented populations, addressing a critical need in reducing health disparity.