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: 10186828
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186828
• 关键词: Address; Anxiety; Anxiety Disorders; Architecture; Big Data; Clinic; Clinical; Clinical Data; Collaborations; Complex; Computerized Medical Record; Data; Data Set; Disease; Electronic Health Record; Employment; Environmental Risk Factor; European; Evaluation; Feeling suicidal; Funding; General Population; Genetic; Genetic Determinism; Genetic Research; Genetic Variation; Genotype; Geography; Goals; Health Care Costs; 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; base; biobank; care outcomes; clinical care; clinical practice; cohort; comorbidity; deep learning; disorder risk; functional disability; genetic epidemiology; genetic risk factor; genome wide association study; genome-wide; health care service utilization; improved; infancy; interest; large datasets; learning strategy; mortality; mortality risk; neuropsychiatric disorder; pleiotropism; polygenic risk score; population based; predict clinical outcome; 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）是常见的、复杂的、 经常共同出现并与显著功能障碍相关的精神病学特征， 医疗保健利用率和成本，以及更高的死亡风险。这三种情况不仅在中国非常普遍， 并产生巨大的社会负担，但我们的团队和其他人最近的研究表明， 显示来自共同遗传变异的共享协方差显著有助于这些精神疾病， 合并症。需要大量的数据集来了解遗传学， 包括多基因风险评分（PRSs）和健康的社会决定因素，例如就业和 教育程度，可以影响这些精神疾病的风险和临床结果， 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 对精神疾病的减贫战略才刚刚开始探索。使用电子健康记录的研究 （EHR）提供了大量数据集的承诺，以检查在以下人群中观察到的患者队列中的这些关系： 临床实践然而，EHR在精神疾病及其相关疾病的研究中的应用尚处于起步阶段。 治疗这项研究将解决关键的知识差距在“基因型-精神病表型” 在人口和地理上多样化的大规模人口样本中的关系 来自哥伦比亚、康奈尔、马约诊所和西奈山四个医疗中心的EHR相关生物库。 我们的目标是：（1）开发用于MDD、焦虑和SUD的EHR表型分型的改进方法， 相关成果的基础上的数据集>30万EHR，（2）评估减贫战略之间的关联， 这些条件，和（3）评估PRS和结果之间的关联，包括治疗抵抗 抑郁症和医疗保健利用在抑郁症、焦虑症和SUD患者中的作用。减贫战略分析将利用数据 从生物银行与> 50，000人与EHR和GWAS数据。成功完成本研究将 实质性地推进我们对PRS在常见精神疾病中的临床应用的理解， 紊乱