Miller-Fleming Tyne W, Allos Annmarie, Gantz Emily, Yu Dongmei, Isaacs David A, Mathews Carol A, Scharf Jeremiah M, Davis Lea K
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA.
Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
medRxiv. 2023 Feb 23:2023.02.21.23286253. doi: 10.1101/2023.02.21.23286253.
Tics are a common feature of early-onset neurodevelopmental disorders, characterized by involuntary and repetitive movements or sounds. Despite affecting up to 2% of young children and having a genetic contribution, the underlying causes remain poorly understood, likely due to the complex phenotypic and genetic heterogeneity among affected individuals.
In this study, we leverage dense phenotype information from electronic health records to identify the disease features associated with tic disorders within the context of a clinical biobank. These disease features are then used to generate a phenotype risk score for tic disorder.
Using de-identified electronic health records from a tertiary care center, we extracted individuals with tic disorder diagnosis codes. We performed a phenome-wide association study to identify the features enriched in tic cases versus controls (N=1,406 and 7,030; respectively). These disease features were then used to generate a phenotype risk score for tic disorder, which was applied across an independent set of 90,051 individuals. A previously curated set of tic disorder cases from an electronic health record algorithm followed by clinician chart review was used to validate the tic disorder phenotype risk score.
Phenotypic patterns associated with a tic disorder diagnosis in the electronic health record.
Our tic disorder phenome-wide association study revealed 69 significantly associated phenotypes, predominantly neuropsychiatric conditions, including obsessive compulsive disorder, attention-deficit hyperactivity disorder, autism, and anxiety. The phenotype risk score constructed from these 69 phenotypes in an independent population was significantly higher among clinician-validated tic cases versus non-cases.
Our findings provide support for the use of large-scale medical databases to better understand phenotypically complex diseases, such as tic disorders. The tic disorder phenotype risk score provides a quantitative measure of disease risk that can be leveraged for the assignment of individuals in case-control studies or for additional downstream analyses.
抽动是早发性神经发育障碍的常见特征，其特点是不自主的重复性运动或声音。尽管抽动影响多达2%的幼儿且有遗传因素，但潜在病因仍知之甚少，这可能是由于受影响个体之间存在复杂的表型和遗传异质性。
在本研究中，我们利用电子健康记录中的密集表型信息，在临床生物样本库的背景下识别与抽动障碍相关的疾病特征。然后，这些疾病特征被用于生成抽动障碍的表型风险评分。
使用来自三级医疗中心的去识别化电子健康记录，我们提取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，以确定抽动病例与对照（分别为N = 1406和7030）中富集的特征。然后，这些疾病特征被用于生成抽动障碍的表型风险评分，并应用于一组独立的90051名个体。使用先前通过电子健康记录算法整理的一组抽动障碍病例，随后进行临床医生病历审查，以验证抽动障碍表型风险评分。
电子健康记录中与抽动障碍诊断相关的表型模式。
我们的抽动障碍全表型关联研究揭示了69种显著相关的表型，主要是神经精神疾病，包括强迫症、注意力缺陷多动障碍、自闭症和焦虑症。在独立人群中，由这69种表型构建的表型风险评分在经临床医生验证的抽动病例中显著高于非病例。
我们的研究结果支持使用大规模医学数据库来更好地理解表型复杂的疾病，如抽动障碍。抽动障碍表型风险评分为疾病风险提供了一种定量测量方法，可用于病例对照研究中的个体分配或其他下游分析。
