4/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders

4/4：利用与 EHR 相关的生物库进行精神疾病的深度表型分析、多基因风险评分建模和结果分析

• 批准号: 10646457
• 负责人: Jyotishman Pathak
• 金额: $ 40.87万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-05 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646457
• 关键词: Address; Anxiety; Anxiety Disorders; Applied Genetics; Architecture; Big Data; Clinic; Clinical; Clinical Data; Collaborations; Complex; Computerized Medical Record; Data; Data Set; Depressive disorder; Disease; Education; Electronic Health Record; Employment; Environmental Risk Factor; Epidemiology; European ancestry; Evaluation; Feeling suicidal; Funding; General Population; Genetic; Genetic Research; Genetic Variation; Genotype; Geography; Goals; Health system; Heritability; Hospitalization; Individual; Knowledge; Link; Machine Learning; Major Depressive Disorder; Medical; Medical center; Mental Health; Mental disorders; Methods; Modeling; Natural Language Processing; New York City; Outcome; Participant; Patients; Performance; Persons; Phenotype; Population; Population Heterogeneity; Research; Risk; Role; Sampling; Scoring Method; Site; Substance Use Disorder; Suicide attempt; Symptoms; Text; Variant; biobank; care outcomes; clinical care; clinical practice; cohort; comorbidity; cost; deep learning; disorder risk; functional disability; genetic risk factor; genome wide association study; genome-wide; health care service utilization; improved; infancy; interest; large datasets; mortality risk; neuropsychiatric disorder; pleiotropism; polygenic risk score; population based; predict clinical outcome; psychiatric comorbidity; psychogenetics; response; risk prediction; risk stratification; social health determinants; structured data; suicidal behavior; therapy resistant; trait; treatment-resistant depression

PROJECT ABSTRACT Major depressive disorder (MDD), anxiety disorders, and substance use disorders (SUDs) are common, complex psychiatric traits that frequently co-occur and are associated with significant functional impairment, increased healthcare utilization and cost, and higher mortality risk. Not only are these three conditions highly prevalent in the general population and generate a huge societal burden, but recent studies by our team and others have shown that shared covariance from common genetic variation significantly contributes to these psychiatric comorbidities. Large data sets are needed to understand how the multifaceted interplay of genetics, including polygenic risk scores (PRSs), and social determinants of health, such as employment and educational attainment, can impact the risk of these psychiatric disorders and clinical outcomes, such as multiple psychiatric hospitalizations. PRSs have shown potential for risk prediction, but the clinical utility of PRSs for psychiatric conditions is just starting to be explored. Research utilizing Electronic Health Records (EHRs) offers the promise of large data sets to examine these relationships in cohorts of patients seen in clinical practice. However, the use of EHRs is in its infancy in the study of psychiatric disorders and their treatment. This study will address critical knowledge gaps in “genotype-psychiatric phenotype” relationships in large, demographically and geographically diverse population-based samples derived from EHR-linked biobanks across four medical centers - Columbia, Cornell, Mayo Clinic and Mount Sinai. Our objectives are to (1) develop improved methods for EHR phenotyping of MDD, anxiety, and SUDs, and related outcomes based on a data-set of >30 million EHRs, (2) evaluate associations between PRSs and these conditions, and (3) assess the association between PRSs and outcomes including treatment resistance in MDD and healthcare utilization in patients with MDD, anxiety and SUD. The PRS analyses will utilize data from biobanks with >50,000 persons with both EHR and GWAS data. Successful completion of this study will substantially advance our understanding of the clinical utility of PRSs for commonly occurring psychiatric disorders.

项目摘要 重度抑郁症 (MDD)、焦虑症和物质使用障碍 (SUD) 很常见且复杂 经常同时出现并与显着功能障碍相关的精神特征，增加 医疗保健利用率和成本以及更高的死亡风险。这三种情况不仅在 普通民众并产生巨大的社会负担，但我们团队和其他人最近的研究表明 研究表明，共同遗传变异的共同协方差对这些精神疾病有显着影响 合并症。需要大量数据集来了解遗传学的多方面相互作用， 包括多基因风险评分（PRS）和健康的社会决定因素，例如就业和 教育程度可能会影响这些精神疾病的风险和临床结果，例如 多次精神病住院治疗。 PRS 已显示出风险预测的潜力，但临床实用性 针对精神疾病的 PRS 的研究才刚刚开始。利用电子健康记录进行研究 （电子病历）提供了大数据集的希望，可以检查患者群体中的这些关系 临床实践。然而，电子病历在精神疾病及其影响研究中的应用还处于起步阶段。 治疗。这项研究将解决“基因型-精神病表型”方面的关键知识差距 大型、人口统计和地理多样化的人口样本中的关系 来自四个医疗中心（哥伦比亚、康奈尔、梅奥诊所和西奈山）的 EHR 连接生物库。 我们的目标是 (1) 开发用于 MDD、焦虑和 SUD 的 EHR 表型分析的改进方法，以及 基于超过 3000 万个 EHR 数据集的相关结果，(2) 评估 PRS 和 这些条件，以及 (3) 评估 PRS 与结果（包括治疗抵抗）之间的关联 MDD 以及患有 MDD、焦虑症和 SUD 的患者的医疗保健利用。 PRS 分析将利用数据 来自拥有超过 50,000 人的生物银行，同时拥有 EHR 和 GWAS 数据。顺利完成这项研究将 极大地增进了我们对 PRS 对常见精神疾病的临床效用的理解 失调。