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: 10687123
• 负责人: Dokyoon Kim
• 金额: $ 47.5万
• 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/10687123
• 关键词: Algorithms; Big Data; Big Data Methods; Bioinformatics; Caring; Chromosome Mapping; Chronic; Climate; Clinical; Clinical Data; Clinical Informatics; Collection; Communities; Complex; Creativeness; 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; Visit; Visualization software; big biomedical data; biobank; clinical decision support; cohort; comorbidity; data integration; design; disease diagnosis; electronic health 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.

精准医学的转化生物信息学和临床信息学的快速发展， 提供了许多计算和信息学方法，以提供更好的预测，诊断和 治疗策略作为临床实用工具。特别是，高维和大规模 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 将生物医学大数据分析中新发现的知识转化为 支持临床决策。这些大数据集的复杂性和规模 承诺，但也面临着巨大的挑战。作为临床医生关注的重要问题之一， 合并症是一种医学上有据可查的现象， 存在并可能相互作用，从而影响主要临床 条件几项研究表明，在一些共病的情况下， 存在于同一时间，疾病表现的模式不同，从一个慢性疾病， 另因此，显然需要改善对患有多种疾病的个人的护理。 但这样做需要更详细地了解疾病的趋势 比我们现在拥有的更多。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 在人类疾病中的共患病一直是具有挑战性的。幸运的是，在过去， 十年来，已经可以获得全面的疾病诊断数据集，主要是 以电子健康记录（EHR）的数据形式。我们可以用病人的 健康史，以确定合并症，并应用数据驱动的方法来研究疾病 合并症模式，考虑所有可能的疾病合并症。特别是发展中 大规模数据的计算和建模， 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 将阐明潜在的遗传和非遗传因素，影响疾病合并症。 我们将应用两种正交方法来识别合并症：1）源于疾病 共现使用EHR数据单独，和2）来自多效性遗传协会使用 EHR关联生物库数据集。基于网络的方法有可能发现意想不到的 疾病之间的关系。我们的建议最重要的优点之一是， 将单一来源的EHR与基因组数据联系起来;这提供了机会， 重新审视个体水平的基因型和表型数据，以设计更有针对性的 研究并提出更具体的问题。此外，我们的研究结果可用于开发 一种新的合并症风险评分，结合了临床数据和遗传效应， 构成临床预防和监测的新工具。这些目标在很大程度上 随着当今精准医学的发展，治疗和预防的理想设计是 考虑个体患者在遗传、生活方式和环境暴露方面的可变性。

项目成果

Impact of natural selection on global patterns of genetic variation and association with clinical phenotypes at genes involved in SARS-CoV-2 infection.

• DOI: 10.1073/pnas.2123000119
• 发表时间: 2022-05-24
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Polygenic risk for type 2 diabetes, lifestyle, metabolic health, and cardiovascular disease: a prospective UK Biobank study.

2型糖尿病，生活方式，代谢健康和心血管疾病的多基因风险：前瞻性英国生物库研究。

• DOI: 10.1186/s12933-022-01560-2
• 发表时间: 2022-07-14
• 期刊: Cardiovascular diabetology
• 影响因子: 9.3

Genome-wide polygenic risk scores for hypertensive disease during pregnancy can also predict the risk for long-term cardiovascular disease.

妊娠期高血压疾病的全基因组多基因风险评分也可以预测长期心血管疾病的风险。

• DOI: 10.1016/j.ajog.2023.03.013
• 发表时间: 2023
• 期刊: American journal of obstetrics and gynecology
• 影响因子: 9.8
• 作者: Lee,SeungMi;Shivakumar,Manu;Xiao,Brenda;Jung,Sang-Hyuk;Nam,Yonghyun;Yun,Jae-Seung;Choe,EunKyung;Jung,YoungMi;Oh,Sohee;Park,JoongShin;Jun,JongKwan;Kim,Dokyoon
• 通讯作者: Kim,Dokyoon

Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data.

• DOI: 10.1093/bioinformatics/btac822
• 发表时间: 2023-01-01
• 期刊: Bioinformatics (Oxford, England)

A Network-Based Analysis of Disease Complication Associations for Obstetric Disorders in the UK Biobank.

• DOI: 10.3390/jpm11121382
• 发表时间: 2021-12-17
• 期刊: Journal of personalized medicine
• 作者: Sriram V;Nam Y;Shivakumar M;Verma A;Jung SH;Lee SM;Kim D
• 通讯作者: Kim D