A2CPS Genetic Variant Core

遗传变异核心 摘要 慢性疼痛是一个复杂的问题，可能受到多种环境和遗传因素的影响。生长 慢性疼痛发生的遗传基础的证据表明，使用遗传预测因子可能会 区分易患慢性疼痛的患者和易患慢性疼痛的患者。这 项目是OMICS数据生成中心(ODGC)的遗传变异核心(GVC)，面向急性到慢性 疼痛信号(A2CPS)计划。在这项计划中，临床中心将招募和收集临床数据和 来自两个纵向队列的生物流体样本，每个队列有1800名受试者。生物体液样本将分别采集0、3和 急性疼痛发作后6个月，包括特定的外科手术或特定的肌肉骨骼 精神创伤。这些样本将被用来产生多组数据，以验证40个主要结果生物标志物 表明对慢性疼痛的易感性或恢复力，以及确定新的候选对象 生物标志物。对于拟议的遗传变异核心，将在第一年执行的目标1将涉及关闭 与A2CPS计划的其他组成部分合作，建立最终的研究设计和方案。全 A2CPS计划的研究人员将共同努力，建立40个主要结果生物标志物。这个 食品药品监督管理局和临床中心调查人员将共同决定具体的样本类型(S)和 收集/处理/存储方法。ODGC和数据集成资源中心/数据协调 构成部分(DIRC/DCC)调查人员将制定元数据和数据标准以及工作流程 向发改委提交元数据和数据。GVC和ODGC的行政核心将建立 用于样本和数据跟踪以及元数据记录的综合LIMS。GVC和DIRC/DATA 集成和分析组件(DIAC)将建立数据分析管道。目标2和目标3将跨越数年 2-4，目标2重点是3600名参与者样本的DNA分离和遗传变异阵列 由临床中心收集。目标3将包括向发改委/发改委提交元数据和数据， 数据的质量控制，数据的分析和解释。该组件的主要目标是生成 以及提交用于验证预选基因变体的高质量基因变体阵列数据 生物标志物。虽然这项研究不能确定新的遗传关联，但我们预计将延长 除了联合选择的遗传变异生物标记物之外的分析，以包括与 对慢性疼痛的敏感性/恢复力。GVC组件调查人员将参与与 DIRC/DIAC旨在开发由多种生物标记物类型(包括分子， 临床、心理社会和/或成像生物标志物)指示对慢性疼痛的易感性/弹性，这可能 用于制定预防和治疗慢性疼痛的个性化策略。负责这项工作的人员 建议的GVC已获得成功执行该项目所需的技能和知识 参与产生基因变异阵列数据的多个大型项目。

GENETIC VARIANT CORE SUMMARY Chronic pain is a complex problem, likely influenced by multiple environmental and genetic factors. Growing evidence for a genetic basis for developing chronic pain suggests it may be possible to use genetic predictors to differentiate between patients likely to be susceptible versus resilient to the development of chronic pain. This project is the Genetic Variant Core (GVC) of the Omics Data Generation Center (ODGC) for the Acute to Chronic Pain Signatures (A2CPS) Program. In this Program, the Clinical Centers will recruit and collect clinical data and biofluid samples from two longitudinal cohorts of 1800 subjects each. Biofluid samples will be collected 0, 3, and 6 months after an acute pain episode, consisting of a specific surgical procedure or a specific musculoskeletal trauma. These samples will be used to generate multi-omic data to validate 40 primary outcome biomarkers indicating susceptibility or resilience to development of chronic pain, as well as to identify new candidate biomarkers. For the proposed Genetic Variant Core, Aim 1, which will be executed in Year 1, will involve close collaboration with other components of the A2CPS Program to establish the final study design and protocols. All of the A2CPS Program investigators will work together to establish the 40 primary outcome biomarkers. The ODGC and Clinical Center investigators will jointly decide on the specific sample type(s) and collection/processing/storage methods. The ODGC and Data integration Resource Center/Data Coordination Component (DIRC/DCC) investigators will establish Metadata and Data Standards and a workflow for submission of metadata and data to the DCC. The GVC and the Administrative Core of the ODGC will establish an integrated LIMS for sample and data tracking, and recording of metadata. The GVC and DIRC/Data Integration and Analysis Component (DIAC) will establish data analysis pipelines. Aims 2 and 3 will span Years 2-4, with Aim 2 focused on DNA isolation and genetic variant arrays for the 3600 participant samples that will be collected by the Clinical Centers. Aim 3 will encompass submission of metadata and data to the DIRC/DCC, quality control of the data, and data analysis and interpretation. The primary goal of this Component is generation and submission of high-quality gene variant array data for the validation of pre-selected genetic variant biomarkers. While the study is not powered to identify novel genetic associations, we expect to extend the analysis beyond the jointly selected genetic variant biomarkers to include additional variants implicated in susceptibility/resilience to chronic pain. GVC component investigators will participate in integrative analyses with the DIRC/DIAC aimed at developing pain signatures comprised of multiple biomarker types (including molecular, clinical, psychosocial, and/or imaging biomarkers) indicating susceptibility/resilience to chronic pain, which can be used to develop personalized strategies for prevention and treatment of chronic pain. The personnel for this proposed GVC have acquired the necessary skills and knowledge to successfully execute this project from participation in multiple large-scale projects generating genetic variant array data.