3/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders

3/4：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: 10633130
• 负责人: ALEXANDER W CHARNEY
• 金额: $ 42.93万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 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/10633130
• 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 Risk; Genetic Variation; Genotype; Geography; Goals; 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; 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; psychiatric comorbidity; 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)、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 医疗保健利用和成本以及较高的死亡风险不仅在这三种情况中非常普遍。 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 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）以及健康因素的社会决定因素，例如就业和教育 达到，会增加这些精神疾病和临床结果的风险，例如多种 PRS 已显示出风险预测的潜力，但 PRS 的临床实用性 电子健康记录 (EHR) 的使用才刚刚开始对精神疾病的探索。 大数据集来检查临床实践中患者群体中的这些关系。 电子病历在精神疾病及其治疗研究中的应用尚处于起步阶段。 在大规模、人口统计学和 来自四个医学领域与 EHR 相关的生物库的基于地理不同人群的样本 中心 - 哥伦比亚、康奈尔、梅奥诊所和西奈山 我们的目标是 (1) 开发改进的方法。 基于超过 3000 万个数据集，对 MDD、焦虑和 SUD 以及相关结果进行 EHR 表型分析 EHR，(2) 评估 PRS 与这些状况之间的关联，以及 (3) 评估该关联 PRS 与结果之间的关系，包括 MDD 的治疗抵抗和患有 MDD 的患者的医疗保健利用率 MDD、焦虑和 SUD 分析将利用来自拥有超过 50,000 名 EHR 患者的生物库的数据。 和 GWAS 数据的成功完成将产生新的数据，以提高我们的理解。 PRS 对常见精神疾病的临床效用。

项目成果

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data.

编码变量的隐性偏差：解决 EHR-GWAS 数据中结构化偏差的框架。

• 发表时间: 2020-09-30
• 影响因子: 3.5
• 作者: Dueñas, Hillary R;Seah, Carina;Johnson, Jessica S;Huckins, Laura M
• 通讯作者: Huckins, Laura M