Next-generation, pathway-specific, polygenic risk scores

下一代、特定途径、多基因风险评分

• 批准号: 10570896
• 负责人: Paul Francis O'Reilly
• 金额: $ 63.63万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10570896
• 关键词: Adverse effects; Basic Science; Biochemical; Biochemical Pathway; Biological Process; Biomedical Research; Bipolar Disorder; Body mass index; Clinical; Clinical Research; Clinical Trials; Complex; Computer software; Custom; Data; Diagnostic; Disease; Disease susceptibility; Environment; Epigenetic Process; Etiology; Failure; Formulation; Future; Generations; Genetic; Genetic Diseases; Genetic Predisposition to Disease; Genetic Risk; Genomics; Genotype-Tissue Expression Project; Goals; Human; In Vitro; Individual; Intuition; Link; Location; Mental disorders; Modeling; Multiomic Data; Pathway interactions; Phenotype; Play; Population; Prevention; Production; Proxy; Research; Research Personnel; Resources; Risk; Role; Route; Running; Sampling; Schizophrenia; Stratification; Subgroup; Symptoms; Testing; Time; Variant; computerized tools; disorder risk; epigenome; functional genomics; genome wide association study; genome-wide; high risk population; individualized prevention; innovation; insight; multiple omics; new therapeutic target; next generation; novel strategies; patient stratification; patient subsets; personalized medicine; personalized therapeutic; polygenic risk score; portability; precision medicine; prototype; rare variant; risk variant; statistical and machine learning; success; tool; trait; transcriptome; treatment response; user-friendly

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.

项目概要 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倾向估计 这些遗传责任的代理在临床和基础研究中具有广泛的应用。 然而，虽然 PRS 将在未来的生物医学研究中发挥关键作用，但它们目前的情况。 该公式并不理想，因为它无法直接解释遗传病风险的子结构。 我们提案的总体目标是引入新一代特定路径的 PRS， 每个个体都有一个单一的全基因组 PRS，而是一组超过 k 个 PRS。 途径将根据组学数据的多尺度整合、利用共表达来定义。 该项目的关键成果将是产生一个网络、转录组和表观基因组。 强大而全面的特定途径 PRS 计算工具 PRSet，以生物学功能为基础。 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， 导致重要的个体水平信息的丢失，提供遗传责任的特定路径估计， 以可扩展、严格的方式计算，并根据最新的多组学数据，可以使研究人员 为了更好地分解异质复杂疾病，确定解释重叠或的关键途径 疾病之间的差异，并解释 PRS 在人群之间和人群内的可移植性问题。 应用我们的路径特异性 PRS 工具，我们寻求根据患者的情况将其分为更同质的亚组 我们将使用 PRSet 以三种方式进行分层：(i) 在 SCZ/BiP 内分层、测试。 如果不同途径的责任形成多种疾病途径，(ii) 区分 SCZ 和 BiP，测试 如果关键途径能够区分这些高度重叠的疾病，(iii) 测试治疗是否存在差异 反应可以通过路径责任来解释，这种分层有助于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 的迅速发展意味着 PRS 的任何进步 该方法将对精神病学研究产生直接、重大的影响。 如果 PRSet 揭示了遗传责任，则可以得出无法通过全基因组 PRS 回答的假设。 比目前的模型更加分层，那么这就需要关注路径及其多组学 整合，开辟精准医疗新路径。 我们的建议是创新的，因为我们开发了第一个针对特定途径、功能信息的 PRS 工具，我们 提出疾病风险可能受到多种遗传倾向的影响，我们根据以下因素对患者进行分层 首次发现途径特异性遗传风险。 总之，我们的建议为该领域提供了一个工具，可以更好地执行强大的路径 PRS 分析 了解疾病的遗传倾向，这可能为精准医学提供更直接的途径。