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

项目总结 多基因风险评分(Prs)的主要吸引力在于提供个体水平的遗传风险估计。 复杂的疾病。这些遗传易感性的代理使基础研究和临床的大量应用成为可能。 设置。然而，尽管PR将在未来的生物医学研究中发挥关键作用，但他们的现状 对于在不同和混合的人群中应用来说，配方是次优的。 为了解决这个问题，我们建议开发高分辨率建模来优化整个PRSS的计算 多样化的种群，这将：(I)使用贝叶斯分层模型来解释种群遗传 和群体间可携带性低的统计原因，(Ii)将遗传风险解构为共同的， 祖先特有的和基因*环境子组件，(Iii)产生基于途径的PRS，这可以帮助 揭示可携带性问题的功能来源，并解释祖先疾病的异质性。钥匙 交付成果将是为不同和混杂的人群量身定做的一套强大的PRS工具的生产。 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 构成了严重的信息损失。通过对这些高分辨率数据进行统计原则性和 通过严谨的方式，研究人员将能够在所有人类群体中执行强大的PRS预测 以及在所有个人身上。这将提供前所未有的预测能力和对疾病机制的洞察。 在目标1中，我们开发了一种贝叶斯层次粗糙集方法BridgePRS3，该方法对LD效应差异进行建模 祖先及其组成的亚祖先之间的大小和等位基因频率。在《目标2》中，我们构建了一部小说 一种用于将基因组解构为本地祖先的混合个体的方法，ADMIXPR 对应于亚祖先的区域，说明了已知的混合历史，并分解了遗传风险 分成3个子组件。在目标3中，我们开发了一种适用于不同种群的基于路径的PRS方法，PRSet+。 最后，我们将构建一个统一的prs方法，global alprs，它可以计算任何个人、任何祖先的prs。 我们的建议意义重大，因为PR的蓬勃应用意味着缩小PR中的差距 预测力将在不同的人群中产生立竿见影的影响。通过执行高分辨率建模 通过反映人类种群结构和暴露基因*环境来提高PRS预测能力 和路径级别对可移植性问题的贡献，我们的PRS工具套件有可能 提高PR的临床实用性，以及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粗糙集工具来模拟高 不同和混合种群的分辨率结构，涉及：(1)祖先(建立亚祖先模型)， 遗传风险(三组分混合PR模型)、基因组(通道级PR)和表型(亚型)。 总而言之，我们的提案将向现场提供一套工具，以执行各种不同的强大的PRS分析 和混合种群，并更好地了解PR的全球异质性、特征和疾病。

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.