Reddy India A, Han Lide, Sanchez-Roige Sandra, Niarchou Maria, Ruderfer Douglas M, Davis Lea K
Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Am J Med Genet B Neuropsychiatr Genet. 2025 Jun;198(4):e33020. doi: 10.1002/ajmg.b.33020. Epub 2025 Jan 2.
Externalizing traits and behaviors are broadly defined by impairments in self-regulation and impulse control that typically begin in childhood and adolescence. Externalizing behaviors, traits, and symptoms span a range of traditional psychiatric diagnostic categories. In this study, we sought to generate an algorithm that could reliably identify transdiagnostic childhood-onset externalizing cases and controls within a university hospital electronic health record (EHR) database. Within the Vanderbilt University Medical Center (VUMC) EHR, our algorithm identified cases with a clinician-validated positive predictive value of 90% and controls with a negative predictive value of 88%. In individuals of genetically defined European ancestry (CEU-clustered; N = 487, N = 5638), case status was significantly associated with psychiatric comorbidity and with elevated externalizing polygenic scores (OR: 1.20; 95% CI: 1.09-1.33; p = 1.14 × 10; based on published genome-wide association data). To test whether our cohort definitions could be applied to generate novel genetic insights, we examined rare (allele frequency < 0.5%) copy number variation. An association (OR: 9.70; CI: 3.24-29.0) was identified in the CEU-clustered cohort on chromosome 2 (chr2: 45,408,678-45,551,530; duplication), although the statistical strength of this association was modest (p = 0.052). We also examined the role of an externalizing burden score based on the number of externalizing diagnoses present in cases and found similar results to our case-control analysis. This analysis identified several other statistically significant CNV region associations. This study provides a framework for identifying childhood externalizing case-control cohorts within an EHR. Future work should validate this framework within other health systems. A broadly applicable algorithm, like this one, may allow for detection of rare outcomes or outcomes in populations historically excluded from genomic research through meta-analysis of data across health care systems.
外化特质和行为的广义定义是自我调节和冲动控制受损，这种情况通常始于童年和青春期。外化行为、特质和症状涵盖了一系列传统的精神科诊断类别。在本研究中，我们试图生成一种算法，该算法能够在大学医院电子健康记录（EHR）数据库中可靠地识别跨诊断的儿童期起病的外化病例和对照。在范德堡大学医学中心（VUMC）的EHR中，我们的算法识别出临床验证的阳性预测值为90%的病例和阴性预测值为88%的对照。在基因定义的欧洲血统个体（CEU聚类；N = 487，N = 5638）中，病例状态与精神科共病以及外化多基因评分升高显著相关（OR：1.20；95% CI：1.09 - 1.33；p = 1.14 × 10；基于已发表的全基因组关联数据）。为了测试我们的队列定义是否可用于产生新的遗传见解，我们检查了罕见（等位基因频率 < 0.5%）拷贝数变异。在CEU聚类队列中，在2号染色体（chr2：45,408,678 - 45,551,530；重复）上发现了一种关联（OR：9.70；CI：3.24 - 29.0），尽管这种关联的统计强度适中（p = 0.052）。我们还根据病例中外化诊断的数量检查了外化负担评分的作用，发现结果与我们的病例对照分析相似。该分析确定了其他几个具有统计学意义的CNV区域关联。本研究提供了一个在EHR中识别儿童外化病例对照队列的框架。未来的工作应在其他卫生系统中验证该框架。像这样一种广泛适用的算法，可能通过对跨医疗系统的数据进行荟萃分析，检测出罕见结局或历史上被排除在基因组研究之外的人群中的结局。
