1/4 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因素的社会决定因素，如就业和教育 达到，会增加这些精神障碍的风险和临床结果，如多发性 精神科住院治疗。PRSS已显示出风险预测的潜力，但PRSS在临床上的应用 对精神疾病的研究才刚刚开始。电子健康记录(EHR)的使用带来了希望 在临床实践中看到的患者队列中检查这些关系的大型数据集。然而， EHR在精神疾病及其治疗研究中的应用尚处于初级阶段。这项研究将解决 在大规模、人口学和人口学研究中“基因-精神病表型”关系的关键知识差距 来自四个医疗机构的EHR连锁生物库的基于地理多样性的人群样本 中心-哥伦比亚大学、康奈尔大学、梅奥诊所和西奈山。我们的目标是(1)开发改进的方法 基于3000万人的数据集，对MDD、焦虑症和SODS的EHR表型和相关结果进行研究 EHR，(2)评估PRSS与这些情况之间的关联，以及(3)评估关联 慢性阻塞性肺疾病患者的PRSS与结果之间的关系，包括治疗耐药性和医疗利用 MDD、焦虑和躯体衰弱。PRS分析将利用来自生物银行的数据，这些数据包括50,000名既有EHR又有EHR的人 和GWAs数据。这项研究的成功完成将产生新的数据，以提高我们对 PRSS治疗常见精神障碍的临床应用。

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.