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

精神分裂症（SCZ）和重度抑郁症（MDD）具有高度遗传性， 终生患病率分别为~1%和15%。这两种疾病都携带大量的 发病率和死亡率，并与严重的社会和个人成本有关。尽管有 对于这两种疾病的有效治疗，约1/3的个体将无法实现症状改善 即使是在多轮药物治疗之后。确定接受此类治疗的风险较大的个人 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。 一个新兴的文献已经确定了与治疗反应相关的基因组变异。在 特别是，抗抑郁反应被认为是高度遗传的;来自 啮齿动物研究同样表明，抗精神病药和抗抑郁药反应表型受到影响， 遗传变异。然而，迄今为止的治疗研究在识别变异方面取得的成功微乎其微 与精神反应有关，可能是由于样本量有限：需要事先努力 序贯治疗试验和前瞻性评估，以表征结局。纵向电子 健康记录（EHR）数据提供了一个机会，有效地表征治疗反应，在许多 现实世界中的个人。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，最终达到足够的权力，以检测现实的影响大小。 研究人员现在建议将这些方法应用于两个大型区域卫生机构的EHR。 系统，每个都连接到一个大型生物库，以调查SCZ和MDD的治疗耐药性。他们将 应用治疗抵抗的典型指标--氯氮平治疗SCZ， MDD的ECT治疗（ECT）-识别与高概率MDD相关的编码和未编码临床特征， EHR数据中的治疗抗性。这些预测因素本身将为以下方面提供有用的基线： 识别高危人群。然后，他们将应用这些来研究每个国家的整个受影响人口， 生物银行，扩展现有的基因组数据与其他基因组范围的关联，产生超过 26，000名抗抑郁药治疗者和2，500名抗精神病药治疗者。而不是简单 进行一项病例对照研究，他们将把治疗抗性作为一种数量性状进行研究， 研究人员开发的方法，并显示出大大增加了这些特征的力量。 该项目结合了临床信息学，机器学习和大型分析的专业知识。 规模基因组学，以及特定领域的专业知识，在精神病治疗的阻力。跨越两 不同的卫生系统，算法和方法开发具有最大的可移植性，促进下一个- 逐步调查。成功识别风险变异将有助于临床风险分层 以及对治疗抗性的生物学基础的研究。

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.