2/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders

项目摘要 重度抑郁障碍(MDD)、焦虑症和物质使用障碍(SODS)是常见的、复杂的 经常同时出现并与严重功能障碍相关的精神特征，增加 医疗保健利用率和成本，以及更高的死亡率风险。这三种情况不仅在 对普通人群造成巨大的社会负担，但我们团队和其他人最近的研究已经 共同遗传变异的共同协方差显著地导致了这些精神疾病 合并症。需要大量的数据集来理解遗传学的多方面相互作用， 包括多基因风险评分和健康的社会决定因素，如就业和 受教育程度会影响这些精神疾病的风险和临床结果，例如 作为多次精神科住院治疗。PRSS已显示出风险预测的潜力，但其临床实用价值 对精神疾病的PRS的研究才刚刚开始。利用电子健康档案进行研究 (EHR)提供了大量数据集的承诺，以在下列患者队列中检查这些关系 临床实践。然而，在精神障碍及其相关疾病的研究中，EHR的使用还处于起步阶段。 治疗。这项研究将解决“基因-精神病学表型”方面的关键知识差距。 基于人口统计和地理位置不同的大样本中的关系 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链接生物库。 我们的目标是(1)开发用于MDD、焦虑和肥胖症的EHR表型的改进方法，以及 基于3000万EHR的数据集的相关结果，(2)评估PRSS和 这些情况，以及(3)评估PRS与包括治疗耐药性在内的结果之间的关联 在MDD、焦虑症和SUD患者的MDD和保健利用方面。方案分析将利用数据 来自生物库，拥有50,000名同时拥有电子健康记录和全球气候变化数据的人。成功完成这项研究将 大大提高了我们对PRSS治疗常见精神疾病的临床效用的理解 精神错乱。

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.