BridgePRS: bridging the gap in polygenic risk scores between ancestries.

BridgePRS：缩小祖先之间多基因风险评分的差距。

• 批准号: 10737057
• 负责人: Paul Francis O'Reilly
• 金额: $ 64.29万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737057
• 关键词: 3-Dimensional; Accounting; Address; Admixture; African; African American; American; Basic Science; Bayesian Modeling; Biomedical Research; Clinic; Clinical; Collection; Complex; Computer software; Data; Data Set; Development; Disease; Environment; Etiology; European; European ancestry; Failure; Formulation; Future; Gene Frequency; Genes; Genetic; Genetic Models; Genetic Risk; Genome; Goals; Heterogeneity; Human; Individual; Methods; Modeling; Pathway interactions; Phenotype; Play; Population; Population Genetics; Population Heterogeneity; Production; Proxy; Randomized; Recording of previous events; Reporting; Research; Research Personnel; Resolution; Risk; Role; Sampling; Scoring Method; Software Tools; Source; Statistical Models; Structure; Target Populations; Testing; Time; computerized tools; design; disease heterogeneity; disorder risk; disparity reduction; flexibility; gene environment interaction; genetic predictors; genome wide association study; genome-wide; improved; innovation; insight; method development; next generation; novel; polygenic risk score; portability; precision medicine; rapid growth; risk variant; tool; trait

PROJECT SUMMARY The key appeal of polygenic risk scores (PRS) is the provision of individual-level estimates of genetic liability to complex disease. These proxies of genetic liability enable a raft of applications across basic research and clinical settings. However, while PRS are set to play a pivotal role in the future of biomedical research, their present formulation is suboptimal for application across diverse and admixed populations. To address this we propose to develop high-resolution modeling to optimize the computation of PRSs across diverse populations, which will: (i) use Bayesian hierarchical modeling to account for the population genetic and statistical causes of low PRS portability between populations, (ii) deconstruct genetic risk into shared, ancestry-specific and gene*environment sub-components, (iii) produce pathway-based PRSs that can help expose the functional sources of the portability problem and explain ancestry disease heterogeneity. The key deliverable will be the production of a suite of powerful PRS tools tailored to diverse and admixed populations. The rationale is that failure to model important structural features that are inherent to diverse clinical populations constitutes a vital loss of information. By modeling this high-resolution data in statistically principled and rigorous ways, researchers will be equipped to perform powerful PRS prediction across all human populations and in all individuals. This will offer unprecedented predictive power and insights into disease mechanisms. In Aim 1, we develop a Bayesian hierarchical PRS method, BridgePRS3, that models differences in LD, effect sizes and allele frequencies between ancestries and their constituent sub-ancestries. In Aim 2, we build a novel method, admixPRS, for application to admixed individuals that deconstructs the genome into local ancestry tracts corresponding to sub-ancestries, accounting for known admixture history, and decomposing genetic risk into 3 sub-components. In Aim 3, we develop a pathway-based PRS method for diverse populations, PRSet+. Finally, we will build a unifying PRS method, globalPRS, that calculates PRS in any individual, of any ancestry. Our proposal is significant because the burgeoning application of PRS means that reducing disparity in PRS predictive power will have immediate, high impact in diverse populations. By performing high-resolution modeling to boost PRS predictive power by mirroring the structure of human populations, and exposing gene*environment and pathway-level contributions to the PRS portability problem, our suite of PRS tools have the potential to increase the clinical utility of PRS and our understanding of how genetic risk varies in global populations. Our proposal is innovative because we develop the first Bayesian hierarchical PRS tools to model the high- resolution structure of diverse and admixed populations, in relation to: (i) ancestry (modeling sub-ancestries), genetic risk (3-component admixPRS model), the genome (pathway-level PRS), and phenotype (sub-types). In summary, our proposal will deliver a suite of tools to the field to perform powerful PRS analyses in diverse and admixed populations and to better understand global heterogeneity of PRS, traits and diseases.

项目摘要 多基因风险评分（PRS）的主要吸引力是提供个体水平的遗传易感性估计， 复杂的疾病这些遗传易感性的代理使基础研究和临床应用成为可能。 设置.然而，尽管PRS将在未来的生物医学研究中发挥关键作用，但它们目前的 制剂对于在不同和混合的群体中应用是次优的。 为了解决这个问题，我们建议开发高分辨率建模，以优化跨 不同的人群，这将：（i）使用贝叶斯分层模型来解释人群遗传 和人口之间PRS可移植性低的统计原因，（ii）将遗传风险解构为共享， 祖先特异性和基因 * 环境子成分，（iii）产生基于路径的PRS，可以帮助 揭示携带问题的功能来源，并解释祖先疾病的异质性。关键 可交付成果将是制作一套针对不同和混合人口的强大减贫战略工具。 其基本原理是，未能对不同临床人群固有的重要结构特征进行建模 会造成重大的信息损失通过在统计学原理和 严格的方法，研究人员将能够在所有人群中进行强大的PRS预测 和所有的个体。这将为疾病机制提供前所未有的预测能力和见解。 在目标1中，我们开发了一种贝叶斯分层PRS方法BridgePRS 3，该方法对LD效应的差异进行建模 大小和等位基因频率之间的祖先和他们的组成亚祖先。在目标2中，我们构建一个小说 方法admixPRS，用于将基因组解构为本地祖先的混合个体 对应于亚祖先的区域，说明已知的混合物历史，并分解遗传风险 分为3个子组件。在目标3中，我们开发了一个基于路径的PRS方法，用于不同的人群，PRSet+。 最后，我们将构建一个统一的PRS方法globalPRS，它可以计算任何祖先的任何个体的PRS。 我们的建议是重要的，因为生产者责任计划的应用迅速发展，意味着减少生产者责任计划的差距 预测能力将在不同人群中产生即时的高度影响。通过执行高分辨率建模 通过反映人类群体的结构和暴露基因 * 环境来提高PRS预测能力 和路径级贡献的PRS可移植性问题，我们的PRS工具套件有潜力， 增加PRS的临床实用性和我们对全球人群中遗传风险如何变化的理解。 我们的建议是创新的，因为我们开发了第一个贝叶斯分层PRS工具来模拟高- 多样性和混合种群的分辨率结构，涉及：（i）祖先（模拟亚祖先）， 遗传风险（3-组分混合PRS模型）、基因组（途径水平PRS）和表型（亚型）。 总之，我们的提案将为现场提供一套工具，以在各种环境中执行功能强大的PRS分析。 和混合人群，更好地了解全球的PRS，性状和疾病的异质性。