Clinical Research Fellow, Neuropsychiatric Genetics Research Group, Department of Psychiatry, School of Medicine, Trinity College Dublin, Ireland.
Assistant Professor, Neuropsychiatric Genetics Research Group, Department of Psychiatry, School of Medicine, Trinity College Dublin, Ireland.
Br J Psychiatry. 2020 May;216(5):275-279. doi: 10.1192/bjp.2019.262.
Copy number variants (CNVs) play a significant role in disease pathogenesis in a small subset of individuals with schizophrenia (~2.5%). Chromosomal microarray testing is a first-tier genetic test for many neurodevelopmental disorders. Similar testing could be useful in schizophrenia.
To determine whether clinically identifiable phenotypic features could be used to successfully model schizophrenia-associated (SCZ-associated) CNV carrier status in a large schizophrenia cohort.
Logistic regression and receiver operating characteristic (ROC) curves tested the accuracy of readily identifiable phenotypic features in modelling SCZ-associated CNV status in a discovery data-set of 1215 individuals with psychosis. A replication analysis was undertaken in a second psychosis data-set (n = 479).
In the discovery cohort, specific learning disorder (OR = 8.12; 95% CI 1.16-34.88, P = 0.012), developmental delay (OR = 5.19; 95% CI 1.58-14.76, P = 0.003) and comorbid neurodevelopmental disorder (OR = 5.87; 95% CI 1.28-19.69, P = 0.009) were significant independent variables in modelling positive carrier status for a SCZ-associated CNV, with an area under the ROC (AUROC) of 74.2% (95% CI 61.9-86.4%). A model constructed from the discovery cohort including developmental delay and comorbid neurodevelopmental disorder variables resulted in an AUROC of 83% (95% CI 52.0-100.0%) for the replication cohort.
These findings suggest that careful clinical history taking to document specific neurodevelopmental features may be informative in screening for individuals with schizophrenia who are at higher risk of carrying known SCZ-associated CNVs. Identification of genomic disorders in these individuals is likely to have clinical benefits similar to those demonstrated for other neurodevelopmental disorders.
拷贝数变异(CNVs)在一小部分精神分裂症患者(约 2.5%)的疾病发病机制中起着重要作用。染色体微阵列检测是许多神经发育障碍的一线遗传检测方法。类似的检测方法在精神分裂症中可能也很有用。
确定临床上可识别的表型特征是否可用于在大型精神分裂症队列中成功模拟与精神分裂症相关的(SCZ 相关)CNV 携带者状态。
逻辑回归和接收者操作特征(ROC)曲线测试了在 1215 名精神病患者的发现数据集中,易于识别的表型特征在模拟 SCZ 相关 CNV 状态方面的准确性。在第二个精神病数据集中(n = 479)进行了复制分析。
在发现队列中,特定学习障碍(OR = 8.12;95%CI 1.16-34.88,P = 0.012)、发育迟缓(OR = 5.19;95%CI 1.58-14.76,P = 0.003)和合并神经发育障碍(OR = 5.87;95%CI 1.28-19.69,P = 0.009)是建模 SCZ 相关 CNV 阳性携带者状态的显著独立变量,ROC 下面积(AUROC)为 74.2%(95%CI 61.9-86.4%)。从发现队列构建的包含发育迟缓合并神经发育障碍变量的模型,在复制队列中产生了 83%(95%CI 52.0-100.0%)的 AUROC。
这些发现表明,仔细记录特定神经发育特征的临床病史可能有助于筛选携带已知 SCZ 相关 CNV 的精神分裂症患者,其风险更高。在这些个体中识别基因组疾病可能具有类似于其他神经发育障碍的临床益处。