Next-generation, pathway-specific, polygenic risk scores

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大量的临床和基础研究应用成为可能 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 该公式是次优的，因为它不能直接说明遗传疾病风险的子结构。 我们建议的总体目标是引入新一代特定路径的生产者责任计划， 生物功能。相反，每个个体都有一个单一的基因组范围的PRS，他们将有一组k个PRS超过k 途径。将根据组学数据的多尺度整合，利用共表达来定义途径。 网络、转录组和表观基因组。该项目的主要交付成果将是制作一个 功能强大且全面的特定路径PRS计算工具PRSet，由生物功能提供信息。 其基本原理是，PRS通过汇总全基因组范围内所有风险变异的影响来计算个体， 导致重要的个人信息丢失。提供特定途径的遗传责任估计数， 以一种可扩展的、统计上严格的方式计算，并以最新的多组学数据为依据， 为了更好地分解异质性复杂疾病，确定解释重叠的关键途径， 疾病之间的差异，并解释问题的PRS之间和人群内的便携性。 应用我们的路径特异性PRS工具，我们试图将患者按其 对关键路径的责任。我们将以三种方式使用PRSet进行分层：（i）在SCZ/BiP内分层，测试 如果对不同途径的易感性形成多种疾病途径，（ii）区分SCZ和BiP， 如果关键途径区分这些高度重叠的疾病，（iii）测试治疗中的变化是否 反应可以解释为途径责任。这种分层有助于解释过去的成功、失败 和副作用的临床试验，并提供新的治疗目标，为患者的子集。 我们的建议意义重大，因为生产者责任计划的应用迅速发展，意味着生产者责任计划的任何进展 这种方法将对整个精神病学研究产生直接的、巨大的影响。特定途径的PRS可以开放 这些假设无法通过全基因组PRS来回答。如果PRSet显示遗传易感性是 比目前的模型更加分层，那么这将需要关注途径及其多组学 整合，为精准医疗铺平了新的道路。 我们的建议是创新的，因为我们开发了第一个路径特定的，功能知情的PRS工具，我们 提出疾病风险可能受到多种遗传责任的影响，我们根据以下因素对患者进行分层： 第一次发现了特定的遗传风险。 总之，我们的建议为现场提供了一个工具，可以更好地执行强大的路径PRS分析， 了解疾病的遗传易感性，这可能为精准医疗提供更直接的途径。

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.