背景:自闭症谱系障碍(ASD)和智力障碍(ID)是异质性和复杂的发育性疾病,具有重要的遗传背景和遗传易感位点的重叠。已知拷贝数变体(CNV)是这些损伤的常见原因。然而,两种疾病的临床异质性导致CNV分析的诊断效能适中.这可以通过根据患者的临床特征对患者进行分层来解决。
目标:首先,我们试图评估在不同的ID和ASD患者组中检测致病性CNVs的特定临床特征的显著性,并确定这些组之间在这些变量的显著性上是否存在差异以及如何存在差异.第二,我们旨在建立一个统计模型,显示特定临床特征如何影响致病性CNV发现的概率.
方法:我们测试了204例ID(N=90)和ASD(N=114)患者的致病性CNVs。我们根据两组的临床特征进行分层。Fisher精确检验用于确定这些变量对致病性CNV结果的显著性。Logistic回归用于创建致病性CNV发现的统计模型。
结果:ID组的致病性CNV的频率明显高于ASD组:18(19.78%)对8(7%)(p<0.004)。根据Fisher精确检验,小头畸形与ID患者的致病结果显着相关(p<0.01),而在ASD患者中,癫痫与致病结果显着相关(p<0.01)。ASD患者发生癫痫时,致病性CNV发现的可能性比癫痫与ID同时发生的可能性高两倍以上(29.6%/14.0%)。面部畸形是检测两组致病性CNV的重要变量(IDp=0.05,ASDp=0.01)。然而,与ASD组相比,ID组的致病性CNV检测概率增加了近两倍(44.4%/23.7%)。通过逻辑回归分析,ASD组出现大头畸形的概率为25%,但根据费希尔的精确检验,这是微不足道的。在没有畸形的情况下,检测致病性CNV的概率降低高达1%,大头畸形,ASD组的癫痫。
结论:错态,小头畸形,癫痫增加ID和ASD患者中致病性CNV发现的概率。每个特征作为致病性CNV检测的预测因子的重要性取决于患者是否仅患有ASD或ID而不同。致病性CNV发现没有畸形的概率,大头畸形,或ASD患者的癫痫发生率较低。因此,CNV分析的功效在这些患者中是有限的。
BACKGROUND: Autism spectrum disorders (ASD) and intellectual disabilities (ID) are heterogeneous and complex developmental diseases with significant genetic backgrounds and overlaps of genetic susceptibility loci. Copy number variants (CNVs) are known to be frequent causes of these impairments. However, the clinical heterogeneity of both disorders causes the diagnostic efficacy of CNV analysis to be modest. This could be resolved by stratifying patients according to their clinical features.
OBJECTIVE: First, we sought to assess the significance of particular clinical features for the detection of pathogenic CNVs in separate groups of ID and ASD patients and determine whether and how these groups differ from each other in the significance of these variables. Second, we aimed to create a statistical model showing how particular clinical features affect the probability of pathogenic CNV findings.
METHODS: We tested a cohort of 204 patients with ID (N = 90) and ASD (N = 114) for the presence of pathogenic CNVs. We stratified both groups according to their clinical features. Fisher\'s exact test was used to determine the significance of these variables for pathogenic CNV findings. Logistic regression was used to create a statistical model of pathogenic CNV findings.
RESULTS: The frequency of pathogenic CNV was significantly higher in the ID group than in the ASD group: 18 (19.78%) versus 8 (7%) (p < 0.004). Microcephaly showed a significant association with pathogenic findings in ID patients (p < 0.01) according to Fisher\'s exact test, whereas epilepsy showed a significant association with pathogenic findings in ASD patients (p < 0.01). The probability of pathogenic CNV findings when epilepsy occurred in ASD patients was more than two times higher than if epilepsy co-occurred with ID (29.6%/14.0%). Facial dysmorphism was a significant variable for detecting pathogenic CNVs in both groups (ID p = 0.05, ASD p = 0.01). However, dysmorphism increased the probability of pathogenic CNV detection in the ID group nearly twofold compared to the ASD group (44.4%/23.7%). The presence of macrocephaly in the ASD group showed a 25% probability of pathogenic CNV findings by logistic regression, but this was insignificant according to Fisher\'s exact test. The probability of detecting pathogenic CNVs decreases up to 1% in the absence of dysmorphism, macrocephaly, and epilepsy in the ASD group.
CONCLUSIONS: Dysmorphism, microcephaly, and epilepsy increase the probability of pathogenic CNV findings in ID and ASD patients. The significance of each feature as a predictor for pathogenic CNV detection differs depending on whether the patient has only ASD or ID. The probability of pathogenic CNV findings without dysmorphism, macrocephaly, or epilepsy in ASD patients is low. Therefore the efficacy of CNV analysis is limited in these patients.