Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, Nashville, USA.
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Transl Psychiatry. 2024 Jul 28;14(1):311. doi: 10.1038/s41398-024-03011-w.
Tics are a common feature of early-onset neurodevelopmental disorders, characterized by involuntary and repetitive movements or sounds. Despite affecting up to 2% of children and having a genetic contribution, the underlying causes remain poorly understood. In this study, we leverage dense phenotype information to identify features (i.e., symptoms and comorbid diagnoses) of tic disorders within the context of a clinical biobank. Using de-identified electronic health records (EHRs), we identified individuals with tic disorder diagnosis codes. We performed a phenome-wide association study (PheWAS) to identify the EHR features enriched in tic cases versus controls (n = 1406 and 7030; respectively) and found highly comorbid neuropsychiatric phenotypes, including: obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, and anxiety (p < 7.396 × 10). These features (among others) were then used to generate a phenotype risk score (PheRS) for tic disorder, which was applied across an independent set of 90,051 individuals. A gold standard set of tic disorder cases identified by an EHR algorithm and confirmed by clinician chart review was then used to validate the tic disorder PheRS; the tic disorder PheRS was significantly higher among clinician-validated tic cases versus non-cases (p = 4.787 × 10; β = 1.68; SE = 0.06). Our findings provide support for the use of large-scale medical databases to better understand phenotypically complex and underdiagnosed conditions, such as tic disorders.
抽动是一种常见的早期神经发育障碍特征，表现为无意识的、重复的运动或声音。尽管抽动障碍影响高达 2%的儿童，且具有遗传贡献，但潜在原因仍知之甚少。在这项研究中，我们利用密集的表型信息，在临床生物库中确定抽动障碍的特征（即症状和合并诊断）。我们使用去识别的电子健康记录（EHR），确定了有抽动障碍诊断代码的个体。我们进行了全表型关联研究（PheWAS），以确定抽动病例与对照组（分别为 1406 例和 7030 例）中 EHR 特征的富集情况，发现了高度共患的神经精神表型，包括：强迫症、注意缺陷/多动障碍、自闭症谱系障碍和焦虑症（p<7.396×10）。这些特征（以及其他特征）随后被用于生成抽动障碍的表型风险评分（PheRS），并在 90051 名独立个体中应用。然后，使用由 EHR 算法确定并由临床医生图表审查确认的抽动障碍黄金标准集来验证抽动障碍 PheRS；在经过临床医生验证的抽动病例与非病例中，抽动障碍 PheRS 显著更高（p=4.787×10；β=1.68；SE=0.06）。我们的研究结果为使用大型医疗数据库来更好地理解表型复杂和诊断不足的疾病（如抽动障碍）提供了支持。
