Next-generation, pathway-specific, polygenic risk scores

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临床和基础研究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 公式不是最优的，因为它不能直接考虑遗传疾病风险中的亚结构。 我们建议的总体目标是引入新一代特定于路径的PR，由 生物功能。相反，每个个体都有一个单一的全基因组PR，他们将有一组k个PR 小路。路径将根据组学数据的多尺度集成来定义，利用共同表达 网络、转录组和表观基因组。该项目的主要成果将是生产一种 强大而全面的路径特定的PRS计算工具PRSet，由生物功能提供信息。 其基本原理是，通过聚合全基因组范围内所有风险变异的影响来计算个体的RR， 导致重要的个人级别信息的丢失。提供特定途径的遗传风险估计， 以可扩展的、统计严谨的方式计算，通过最新的多组数据提供信息，可以使研究人员 为了更好地分解异质性复杂疾病，确定解释重叠或 不同障碍之间的差异，并解释了在人群之间和人群内的可携带性问题。 应用我们的路径特定的PR工具，我们试图根据患者的情况将患者分成更同质的亚组 关键道路上的责任。我们将以三种方式使用PRSet进行分层：(I)在SCZ/BIP内分层，测试 如果不同途径的易感性形成多条致病途径，(Ii)区分SCZ和BIP，测试 如果关键途径区分了这些高度重叠的疾病，(Iii)测试治疗中的差异 反应可以用通路的可靠性来解释。这样的分层可以帮助解释过去的成功和失败 和临床试验中的不良反应，并提供针对患者亚群的新治疗靶点。 我们的建议意义重大，因为PRS的迅速应用意味着PRS的任何进步 这种方法将对精神病学研究产生立竿见影的影响。特定路径的PR可能会打开 通向全基因组PR无法回答的假说。如果PRSet揭示遗传责任是 比目前的模型更具层次性，那么这将需要关注路径及其多体 融合，为精准医疗铺平了一条新路。 我们的建议是创新的，因为我们开发了第一个特定于路径、了解功能的PRS工具，我们 提出疾病风险可能受多种遗传责任的影响，我们根据以下因素对患者进行分层 首次发现了特定途径的遗传风险。 总而言之，我们的建议为现场提供了一种工具，可以更好地执行强大的路径粗糙度分析 了解疾病的遗传易感性，这可能会为精确医学提供一条更直接的途径。

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 clinical and basic research settings. However, while PRS are set to play a pivotal role in the future of biomedical research, their present formulation is suboptimal since it fails to directly account for substructure in genetic disease risk. The overarching goal of our proposal is to introduce a new generation of pathway-specific PRS, informed by biological function. Rather a single genome-wide PRS for each individual, they will have a set of k PRS over k pathways. Pathways will be defined according to multiscale integration of ‘omics data, exploiting co-expression networks, the transcriptome and the epigenome. The key deliverable from this project will be the production of a powerful and comprehensive pathway-specific PRS computational tool, PRSet, informed by biological function. The rationale is that PRS calculated for individuals by aggregating the effects of all risk variants genome-wide, results in a loss of vital individual-level information. Providing pathway-specific estimates of genetic liability, computed in a scalable, statistically rigorous way, informed by latest multi-omic data, could enable researchers to better decompose heterogenous complex disease, identify key pathways that explain overlap or differences among disorders, and explain problems of portability of PRS between and within populations. Applying our pathway-specific PRS tool, we seek to stratify patients into more homogenous subgroups by their liability over key pathways. We will use PRSet for stratification in three ways: (i) stratifying within SCZ/BiP, testing if liability over different pathways forms multiple routes to disease, (ii) differentiating between SCZ and BiP, testing if key pathways differentiate these highly overlapping disorders, (iii) testing whether variation in treatment response can be explained by pathway liability. Such stratification could help explain past successes, failures and adverse-effects in clinical trials, and provide new therapeutic targets tailored to subsets of patients. Our proposal is significant because the burgeoning application of PRS means that any advance in the PRS approach will have immediate, high impact across psychiatric research. Pathway-specific PRS could open-up routes to hypotheses that cannot be answered by genome-wide PRS. If PRSet reveals that genetic liability is more stratified than presently modelled, then this would call for a focus on pathways and their multi-omic integration, paving a new path towards precision medicine. Our proposal is innovative because we develop the first pathway-specific, function-informed, PRS tool, we propose that disease risk may be influenced by multiple genetic liabilities, and we stratify patients according to pathway-specific genetic risk for the first time. In conclusion, our proposal delivers a tool for the field to perform powerful pathway PRS analyses, better understand genetic liability to disease, and which may offer a more direct route to precision medicine.