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.

项目摘要 多基因风险评分（PR）的主要外观是提供遗传负债的个体水平估计值 复杂疾病。这些遗传责任的代理可以在临床和基础研究中进行大量应用 设置。但是，尽管PR将在生物医学研究的未来中发挥关键作用，但他们现在 公式是次优的，因为它无法直接考虑遗传疾病风险的子结构。 我们提案的总体目标是引入新一代的特定途径PR，由 生物功能。而是每个个体的一个全基因组PR，它们将在K上有一组K PR 途径。途径将根据“ OMICS数据的多尺度集成”来定义，并利用共表达 网络，转录组和表观基因组。该项目的主要交付将是生产 强大而全面的途径特定PRS计算工具，PRSET，由生物学功能告知。 理由是，通过汇总所有风险变异的所有风险变异的影响， 导致损失重要的个人级别信息。提供者对遗传责任的特定途径估计， 以最新的多族数据告知的可扩展，统计上严格的方式计算，可以使研究人员能够 为了更好地分解异质复杂疾病，请确定解释重叠或 疾病之间的差异，并解释人群之间和内部PR的可移植性问题。 应用我们的途径特异性PRS工具，我们试图通过他们 关键途径的责任。我们将以三种方式使用PRSET进行分层：（i）在SCZ/BIP内进行分层，测试 如果对不同途径的责任构成了多种疾病路线，（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，则（iii）测试治疗中的变化是否变化 响应可以通过路径责任来解释。这种分层可以帮助解释过去的成功，失败 在临床试验中的不良影响，并提供针对患者子集的新治疗靶标。 我们的建议很大 在精神病研究中，方法将立即产生高影响。途径特定的PR可以打开 通往基因组PR无法回答的假设的途径。如果PRSET揭示了遗传责任是 比目前的建模更加分层，然后要求关注途径及其多摩变 整合，粘贴了通往精密医学的新途径。 我们的建议具有创新性，因为我们开发了第一个途径特定，功能信息，PRS工具，我们 提议疾病风险可能受到多种遗传负债的影响，我们根据 第一次特定于途径的遗传风险。 总之，我们的建议为该领域提供了执行强大途径PRS分析的工具，更好 了解对疾病的遗传责任，这可能为精密医学提供更直接的途径。