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: 10372247
• 负责人: Dokyoon Kim
• 金额: $ 32.5万
• 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/10372247
• 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.

翻译生物信息学和临床信息学在精准医学中的快速进展 提供了许多计算和信息学方法，以提供更好的预测、诊断和 作为一种临床实用的治疗策略。尤其是高维、大尺度 生物医学数据集，从临床数据到组学数据，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化为 支持临床决策。这些大数据集的复杂性和规模非常大 承诺，但也带来了巨大的挑战。作为临床医生关注的重要问题之一， 共病在医学上是一种有充分证据的现象，即一种或多种疾病 存在并可能相互作用，从而影响初级临床 条件。几项研究表明，在多种并存条件下， 同时存在，并且疾病的表现形式因一种慢性病而不同 又一个。因此，显然需要改善对多发性硬化症患者的护理。 但要做到这一点，需要对疾病的趋势有更详细的了解 比我们目前拥有的更多的联想。以前的研究主要集中在 少数特定的合并症；调查广泛疾病的根本原因 人类疾病中的共病一直是具有挑战性的。幸运的是，在过去 十年来，疾病诊断数据的全面收集已经成为可能，主要是 以来自电子健康记录(EHR)的数据的形式。回想起来，我们可以用病人的 确定并存疾病的健康史，并应用数据驱动的方法来研究疾病 考虑所有可能的疾病并存的共病模式。特别是，发展中 大规模数据的计算和建模，该数据将新定义的共病模式与 基因组学在揭示疾病的分子机制方面具有巨大的潜力。首先，我们 将阐明影响疾病共病的潜在遗传和非遗传因素。 我们将应用两种正交法来识别合并症：1)源于疾病 仅使用EHR数据进行同现，以及2)使用 EHR链接的生物库数据集。基于网络的方法有可能发现意想不到的 疾病之间的关系。我们的建议最显著的优势之一是 将单一来源的电子病历链接到基因组数据；这提供了机会 重新审视个体水平的基因型和表型数据，以设计出更有针对性的 学习和提出更具体的问题。此外，我们的结果还可用于开发 一种结合了临床数据和遗传效应的新的共病风险评分，这可能 构成了临床预防和监测的新工具。这些目标是非常一致的 在当今精准医疗的氛围下，治疗和预防的理想设计是 考虑单个患者在遗传、生活方式和环境暴露方面的变异性。