Unravelling genetic basis of comorbidity using EHR-linked biobank data

使用与 EHR 相关的生物库数据揭示合并症的遗传基础

• 批准号: 10224747
• 负责人: Dokyoon Kim
• 金额: $ 47.94万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224747
• 关键词: Algorithms; Big Data; Big Data Methods; Bioinformatics; Caring; Chronic; Climate; Clinical; Clinical Data; Clinical Informatics; Collection; Communities; Complex; Data; Data Set; Diagnosis; Disease; Electronic Health Record; Environmental Exposure; Etiology; Genes; Genetic; Genomics; Genotype; Goals; Graph; Health; Human; Individual; Informatics; International Classification of Disease Codes; Knowledge; Life Style; Link; Machine Learning; Maps; Measures; Medical; Medical Genetics; Medicine; Methodology; Methods; Modeling; Molecular; Monitor; Network-based; Participant; Patients; Pattern; Phenotype; Population; Prevention; Recording of previous events; Research; Research Personnel; Risk; Scoring Method; Source; System; Time; Translating; Variant; Veterans; base; big biomedical data; biobank; clinical decision support; cohort; comorbidity; design; disease diagnosis; electronic data; genetic architecture; genetic association; genetic information; genetic variant; genomic data; high dimensionality; human disease; improved; individual patient; interactive tool; large scale data; non-genetic; novel; patient variability; phenome; phenotypic data; pleiotropism; precision medicine; programs; rare variant; supervised learning; tool; treatment strategy; trend

Rapid progress in translational bioinformatics and clinical informatics for precision medicine has provided many computing and informatics methodologies to provide better prediction, diagnosis and treatment strategy as a clinical utility. In particular, high dimensional and large-scale biomedical data sets, ranging from clinical data to ‘omics data, provide an unprecedented opportunity for translating the newly found knowledge from biomedical big data analytics to support clinical decisions. The complexity and scale of these big data sets hold great promise, yet present substantial challenges. As one of important concerns for clinicians, comorbidity is a well- documented phenomenon in medicine in which one or more medical conditions exist and potentially interact with one another, thereby influencing the primary clinical condition. Several studies show variability in the number of comorbid conditions that can exist at one time, and patterns of disease presentation differ from one chronic condition to another. Thus, there is a clear need to improve care for individuals with multiple comorbidities, but doing so requires a much more detailed understanding of the trends of disease associations than we currently possess. Previous studies have primarily focused on a handful of specific comorbidities; investigating the underlying causes of broad disease comorbidity across the human diseasome has been challenging. Fortunately, in the past decade, comprehensive collections of disease diagnosis data have become available, primarily in the form of data from electronic health records (EHRs). Retrospectively, we can use a patient’s health history to identify comorbidities and apply a data-driven approach to studying disease comorbidity patterns that considers all possible disease comorbidities. In particular, developing computing and modeling of large-scale data that integrates newly defined comorbidity patterns with genomics will hold great potential for uncovering molecular mechanisms of disease. Primarily, we will elucidate the underlying genetic and non-genetic factors that influence disease comorbidity. We will apply two orthogonal approaches to identify comorbidities: 1) deriving from disease co-occurrence using EHR data alone, and 2) deriving from pleiotropic genetic associations using the EHR-linked biobank dataset. Network-based approaches have the potential to uncover unexpected relationships between diseases. One of the most significant advantages of our proposal is the linking of a single-source EHR to genomic data; this provides the opportunity to revisit individual-level genotype and phenotype data for the design of more targeted studies and to ask more specific questions. Additionally, our results can be used to develop a novel comorbidity risk score that combines both clinical data and genetic effects, which might constitute a new tool for clinical prevention and monitoring. These goals are very much in keeping with today’s climate of precision medicine, where treatment and prevention are ideally designed to consider an individual patient’s variability in genetics, lifestyle, and environmental exposures.

精确医学的转化生物信息学和临床信息的快速进步已有 提供了许多计算和信息信息，以提供更好的预测，诊断和 治疗策略作为临床实用程序。特别是高维和大规模的 生物医学数据集，从临床数据到“ OMICS数据”，提供了前所未有的 将新发现的知识从生物医学大数据分析转换为 支持临床决策。这些大数据集的复杂性和规模很高 承诺，但面临重大挑战。作为临床医生的重要问题之一， 合并症是一种或多种医疗状况的医学现象 存在并可能彼此相互作用，从而影响主要临床 健康）状况。几项研究表明合并症的数量可变性 一次存在，疾病表现的模式不同于一种慢性条件到 其他。这很明显需要改善对有倍数的人的护理 合并症，但这样做需要更详细地了解疾病的趋势 关联比我们目前拥有的。以前的研究主要集中于 少数特定合并症；调查广泛疾病的根本原因 人类疾病的合并症已受到挑战。幸运的是，过去 十年来，疾病诊断数据的全面收集，主要 以电子健康记录（EHR）的数据形式。回顾性，我们可以使用患者的 识别合并症并采用数据驱动方法来研究疾病的健康病史 考虑所有可能的疾病合并症的合并症模式。特别是发展 大规模数据的计算和建模，将新定义的合并症模式与 基因组学将对揭示疾病的分子机制具有很大的潜力。主要是我们 将阐明影响疾病合并症的潜在遗传和非遗传因素。 我们将采用两种正交方法来识别合并症：1）来自疾病 单独使用EHR数据共同出现，2）使用多效遗传关联衍生 EHR链接的生物银行数据集。基于网络的方法有可能发现意外的 疾病之间的关系。我们提案中最重要的优势之一是 将单源EHR连接到基因组数据；这提供了机会 重新审视个人级基因型和表型数据，以设计更具针对性的设计 研究并提出更具体的问题。此外，我们的结果可用于发展 一种新型合并症风险评分，结合了临床数据和遗传效应，可能 构成用于临床预防和监测的新工具。这些目标非常保持 借助当今的精密药物的气候，理想的治疗和预防旨在 考虑患者在遗传学，生活方式和环境暴露方面的变异性。