1/2 Leveraging electronic health records for pharmacogenomics of psychiatric disorders

精神分裂症(SCZ)和严重抑郁障碍(MDD)是高度可遗传的，使人虚弱 终生患病率分别为~1%和15%。这两种疾病都有实质性的 发病率和死亡率，并与严重的社会和个人成本有关。尽管有空闲时间 在这两种疾病的有效治疗方法中，~1/3的人不会有症状改善 即使在多次用药之后。确定接受此类治疗的风险较高的个人 无反应或治疗抵抗，可能有助于对这些人进行更有针对性的干预。 一篇新兴的文献已经确定了与治疗反应相关的基因组变异。在……里面 特别是，抗抑郁剂的反应被认为是高度可遗传的；来自 啮齿动物研究同样表明，抗精神病药物和抗抑郁药物的反应表型受到影响 通过遗传变异。然而，到目前为止，治疗研究在识别变异方面的成功微乎其微。 与精神药物反应有关，可能是由于样本量有限：需要先前的努力 序贯治疗试验和前瞻性评估，以确定结果。纵向电子化 健康记录(EHR)数据提供了有效描述许多患者治疗反应的机会 真实世界环境中的个人。再加上大型和不断扩大的生物库，这些队列允许低- 成本，大规模的基因组研究，最终实现了足够的能力来检测现实的效果大小。 研究人员现在建议将这些方法应用于两个大型地区性卫生机构的EHR 系统，每个连接到一个大型生物库，以调查SCZ和MDD的治疗耐药性。他们会 应用治疗耐药的典型指标-氯氮平治疗SCZ，以及电惊厥 MDD的治疗(ECT)-确定编码和非编码的临床特征与高概率的 EHR数据中的治疗耐药性。这些预测因素本身将提供一个有用的基准 识别高危个体。然后，他们将应用这些方法来研究每一种病毒的整个受影响人口 Biobank，通过额外的全基因组关联扩展现有的基因组数据，产生超过 26,000名接受抗抑郁药物治疗的个人和2,500名接受抗精神病药物治疗的个人。而不是简单地 在进行一项病例对照研究时，他们将检查治疗耐药性作为一种数量性状，应用 由研究人员开发的方法，并被证明大大增加了这些特征的力量。 该项目结合了临床信息学、机器学习和大型 规模基因组学，以及在精神治疗抵抗方面的领域专门知识。跨越两个 不同的健康系统，开发的算法和方法具有最大的可移植性，促进下一步- 循序渐进的调查。成功识别风险变量将有助于临床风险分层的努力 以及对治疗耐药的生物学基础的调查。

Schizophrenia (SCZ) and major depressive disorder (MDD) are highly heritable, debilitating diseases with lifetime prevalences of ~1% and 15%, respectively. Both disorders carry substantial morbidity and mortality and are associated with severe societal and personal costs. Despite the availability of efficacious treatments for both disorders, ~1/3 of individuals will not achieve symptomatic improvement even after multiple rounds of medication. Identifying individuals at greater risk for such treatment nonresponse, or treatment resistance, could facilitate more targeted interventions for these individuals. A burgeoning literature has identified genomic variation associated with treatment response. IN particular, antidepressant response has been suggested to be highly heritable; convergent data from rodent studies likewise suggest that antipsychotic and antidepressant response phenotypes are influenced by genetic variation. However, treatment studies to date have had minimal success in identifying variants associated with psychotropic response, likely as a result of limited sample sizes: prior efforts required sequential treatment trials and prospective assessment to characterize outcomes. Longitudinal electronic health records (EHR) data provide an opportunity to efficiently characterize treatment response in many individuals in real-world settings. Coupled with large and expanding biobanks, these cohorts allow for low- cost, large-scale genomic studies that finally achieve sufficient power to detect realistic effect sizes. The investigators now propose to apply these approaches to the EHRs of two large regional health systems, each linked to a large biobank, to investigate treatment resistance in SCZ and MDD. They will apply canonical indicators of treatment resistance - clozapine treatment for SCZ, and electroconvulsive therapy (ECT) for MDD - to identify coded and uncoded clinical features associated with high probability of treatment resistance in EHR data. These predictors will themselves provide a useful baseline for identifying high risk individuals. Then, they will apply these to study the entire affected population of each biobank, extending existing genomic data with additional genome-wide association, yielding more than 26,000 antidepressant-treated individuals and 2,500 antipsychotic-treated individuals. Rather than simply conducting a case-control study, they will examine treatment resistance as a quantitative trait, applying a method developed by the investigators and shown to substantially increase power for such traits. The project combines expertise in clinical informatics, machine learning, and analysis of large scale genomics, as well as domain-specific expertise in psychiatric treatment resistance. Spanning two distinct health systems, the algorithms and methods developed have maximal portability, facilitating next- step investigations. Successful identification of risk variants will facilitate efforts at clinical risk stratification as well as investigation of the biology underlying treatment resistance.