1/4 Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders

1/4 利用 EHR 连接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: 10199767
• 负责人: Joseph John Mann
• 金额: $ 26.49万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10199767
• 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 Risk; Genetic Variation; Genotype; Geography; Goals; Health Care Costs; Health system; Heritability; Hospitalization; Individual; Knowledge; Link; Machine Learning; Major Depressive Disorder; Measures; 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; psychogenetics; response; risk prediction; risk stratification; social determinants; social health determinants; structured data; suicidal behavior; technique development; 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 factors, such as employment and educational attainment, can increase 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. Use of 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, as well as (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 generate new data in improving our understanding of the clinical utility of PRSs for commonly occurring psychiatric disorders.

项目摘要 重度抑郁症（MDD），动画障碍和药物使用障碍（SUD）很常见，复杂 经常同时发生并与重大功能障碍相关的精神病特征，增加 医疗保健利用和成本，以及更高的死亡率风险。这三个条件不仅高度普遍 总体人口并产生了一个庞大的社交伯恩，但是我们的团队和其他人的最新研究已经 表明，共同遗传变异的共同协方差显着有助于这些精神病学 合并症。需要大量数据集来了解遗传学的多面相互作用，包括 多基因风险评分（PRS）和健康因素的社会决定者，例如就业和教育 成就，可以增加这些精神疾病和临床结果的风险，例如多重 精神病住院。 PRS显示了风险预测的潜力，但是PRS的临床实用性 精神病刚刚开始探索。电子健康记录的使用（EHRS）提供了承诺 在临床实践中看到的患者中，大量数据集以检查这些关系。但是， EHR的使用仍处于精神疾病及其治疗研究中。这项研究将解决 大，人口统计学和 从四个医学的EHR链接生物库中得出的地理基于人群的样本 中心 - 哥伦比亚，康奈尔，梅奥诊所和西奈山。我们的目标是（1）开发了改进的方法 用于MDD，焦虑和SUDS的EHR表型，以及基于3000万的数据集的相关结果 EHR，（2）评估PRS与这些条件之间的关联，以及（3）评估关联 在PRS和结局之间，包括MDD的治疗耐药性以及患者的医疗保健利用 MDD，焦虑和SUD。 PRS分析将利用来自> 50,000人的生物库的数据 和GWAS数据。这项研究的成功完成将生成新数据，以提高我们对 PRS的临床实用性常见于精神疾病。