Prematurity, ventricular septal defect and dysmorphisms are independent predictors of pathogenic copy number variants: a retrospective study on array-CGH results and phenotypical features of 293 children with neurodevelopmental disorders and/or multiple congenital anomalies

Descriptive statistics were used to present the data. Distribution of continuous data was assessed by Kolomogorov-Smirnov test. Categorical data, such as phenotype characteristics or somatic problems, were presented as frequencies (%) of total number of patients tested, while continuous data (number of genes and CNV size) were presented as median with interquartile range (IQR). Comparison of phenotype characteristics between two groups with negative aCGH or CNVs and between three groups of clinical significance (pathogenic, likely pathogenic and likely benign CNVs) was done using Pearson’s chi-square test or Fisher exact test. Continuous data were compared between two groups using Mann-Whitney U test and among three groups using Kruskal-Wallis test. Post-hoc analysis was applied with Bonferroni correction for all multiple comparisons. Spearman’s correlation analysis was applied to assess association of chromosome size and the number of CNVs per chromosomes. Separate logistic regression analyses were done to assess independent predictors of positive microarray and pathogenic CNVs, respectively. Variables that showed difference at p < 0.1 level in univariate analysis were entered into a multivariate logistic regression model and backward stepwise selection of variables was performed. Odds ratios (OR) with 95% confidence intervals (CI) were computed and the Hosmer-Lemeshow goodness-of-fit test was performed to assess overall model fit. Measures of discrimination (Nagelkerke r² and area under the receiver operating characteristic curve, ROC area) were calculated for all regression models.

All statistical tests were two-sided and were performed at a 5% significance level. SPSS software (version 20.0; SPSS Inc., Chicago, IL, USA) was used for the statistical analysis.

Of the total sample of 339 patients enrolled, 240 (70.8%) presented a genomic rearrangement (CNV), while 99 (29.2%) received a negative aCGH result. Within these two groups, some patients (15 and 31 respectively) subsequently received a different molecular or clinical diagnosis. Therefore, the final sample consisted of 293 patients (225 patients with CNVs and 68 control patients with negative aCGH) (Fig. 1).

The sample presented 169 males (57.5%) and 124 females (42.3%) with an average age at the time of the test of 7 years (range 1 month - 29 years). The majority of patients executed aCGH in the first years of life (38.5%) or at primary school age (27.6%).

Neurodevelopmental disorders (such as psychomotor developmental delay, intellectual disability, autism spectrum disorder, attention deficit and hyperactivity disorder) were observed as the main indication to perform aCGH. Psychomotor developmental delay, in particular with impairment of language (78.5%), and intellectual disability (66.4%) were the most frequently observed. 28.3% of requests concerned isolate NDD, while NDD in combination with other features (dysmorphisms, congenital malformations, epilepsy) reached 83.9%.

As regards to the severity of intellectual disability, 31.1% of the patients had mild ID, 5% of subjects with severe intellectual disability had developmental delay and language impairment, 14% of subjects presented with autism spectrum disorder.

Congenital heart anomalies (atrial septal defect, patent ductus arteriosus), ocular diseases, CNS malformations (corpus callosum, hippocampus or white matter anomalies), EEGraphic abnormalities, face and hands dysmorphisms were the most reported features [Additional file 2: Table S2].

Among the 225 patients with CNVs, 70 (31.1%) showed pathogenic CNVs and 155 (68.8%) carried VOUS (105 likely benign and 50 likely pathogenic). Of the pathogenic CNVs, 27 were associated with known syndromes, 26 were new microdeletions or microduplications containing at least one gene whose haploinsufficiency or amplification correlates with known pathogenic conditions, and 17 were rare de novo CNVs or other chromosomal imbalances (Table 1).

In the total group of patients with chromosomal rearrangements (225), 153 (68%) presented a single CNV (73 deletions and 75 duplications), while the remaining cases had multiple rearrangements, up to 5 CNVs in a single patient. Therefore, the total number of CNVs detected were 323: 81 pathogenic, 72 VOUS likely pathogenic and 170 VOUS likely benign [Additional file 1: Table S1].

We detected 155 deletions and 157 duplications. In addition, we found other anomalies such as amplifications, triplications, tetrasomies or deletions and duplications in mosaic in 4.8% of the patients. 91 CNVs (28%) were de novo, 194 CNVs (60%) were inherited: autosomal inheritance from the father in 95 cases (29%), autosomal inheritance from the mother in 77 cases (24%) and X-linked transmission in 22 cases (7%). The remaining 38 CNVs (12%) were of unknown origin (Fig. 2).

The average size of the CNVs was 2494 Kb (range 18–93,000). The average number of genes located in the CNVs was 16.42 (range 0–485), of which protein coding genes 16.41 (range 0–486) and disease genes 3.58 (range 0–115).

The distribution of CNVs on chromosomes did not appear to be linked to chromosome size or gene density. Notably, we observed a greater concentration of rearrangements on chromosome X (10.8%) and chromosome 1 (9.6%), but the number of CNVs did not positively correlate with the size of the chromosome (r² 0.29). There was no correlation between the number of CNVs and the gene density of the chromosomes (r² 0.006) (Fig. 3a-c).

Pathogenic CNVs mainly clustered on chromosomes 1, 15, 16, 22, and X (Fig. 3d).

Comparing the group of patients with pathogenic CNVs and VOUS, higher statistically significant frequency (p < 0.01) of delayed psychomotor development, intellectual disability, IUGR, prematurity, congenital heart disease, cerebral malformations and dysmorphisms was detected in the pathogenic CNVs group. In addition, a significant difference with p < 0.05 for absence of speech and anomalies of the interventricular septum was found. Somatic overgrowth and autism spectrum disorders were the only two data in which a statistically significant difference (p < 0.05) was found in favor of VOUS. [Additional file 3: Table S3].

Clinical features showing statistically significant differences among patients with pathogenic CNVs, likely pathogenic CNVs and likely benign CNVs are reported in Fig. 4 [complete description in Additional file 4: Table S4].

Lastly, we compared patients with likely pathogenic VOUS [Additional file 5: Table S5] to the group with negative aCGH (comprising likely benign VOUS plus controls). We detected statistically significant differences in favor of likely pathogenic CNVs for abnormal EEG and for limb dysmorphisms [Additional file 6: Table S6].

Multivariate logistic regression analysis of independent predictors of pathogenic CNV was consequently conducted and results are shown in Table 2. Univariate analysis showed that 27 variables were statistically significant predictors of pathogenic CNVs, of which only prematurity, ventricular septal defect (VSD) and dysmorphisms remained significant predictors of pathogenic CNVs in the multivariate logistic model. The Nagelkerke r² for the final model was 0.216, the ROC area was 0.619 (95% CI 0.529–0.710; p < 0.013) and Hosmer-Lemeshow test for goodness of fit was statistically insignificant (p = 0.604).

As regards molecular cytogenetic characteristics, both the size of the rearrangements and the number of contained genes, protein coding genes and disease genes were statistically significant (p < 0.0001) for pathogenic CNVs in the comparison of the three groups (pathogenic, likely pathogenic and likely benign). The number of broken genes is not configured as a significant element. As far as inheritance is concerned, de novo CNVs were represented with statistical significance in the group of pathogenic CNVs; comparing the type of aberration, we found a greater percentage of deletions and fewer duplications in pathogenic CNVs, with statistical significance versus likely benign CNVs (Table 3).

In addition, for those patients (n = 16) who had a single CNV of uncertain significance and not containing any known protein coding genes, we performed an in silico prediction of the noncoding elements and of the possible modification of topologically associating domains (TADs) by consulting the 3D Genome Browser [23] [Additional file 7: Table S7]. This analysis provided some useful insights on CNVs previously dismissed as non-significant, suggesting a novel functional approach that might be included in the current interpretation guidelines.

Then we analyzed the core features (NDD/Dysmorphisms/MCA/Epilepsy) for which aCGH was performed in each patient, as either isolated or associated elements. Comparing these core features with the results of aCGH, we observed that it was more likely to find an abnormal rearrangement when NDD were associated with other features rather than isolated. In patients with NDD alone we observed a statistically significant presence of negative aCGH (p = 0.0003) compared to presence of CNVs, while in patients with a combination of NDD and dysmorphisms we found a statistically significant presence of CNVs (p = 0.0358) [Additional file 8: Table S8]. Specifically, in patients with isolated NDD and presence of CNVs we observed likely benign CNVs more frequently than pathogenic or likely pathogenic CNVs (p < 0.00001); whereas in subjects with NDD associated with dysmorphism, pathogenic CNVs were more likely to be detected (p = 0.0042) (Table 4).

Regarding NDD reported in our sample, individuals with moderate to severe ID were around 20% (59/293) of total patients [Additional file 2: Table S2] and the rate of pathogenic CNVs in this group was 28.8% (17/59). Accordingly, next generation sequencing analysis identified single nucleotide variants in 39% of patients with severe intellectual disability while causative CNVs in only 21% of them [24].

A flow-chart (Fig. 5) regarding the care for patients with NDD and/or MCA and/or dysmorphisms has been made on the basis of the data inferred from this study and their comparison with the literature [14‐20]. Our purpose is to make the investigative process of CNVs more informative and to suggest possible guide elements in aCGH interpretation. It is of primary importance because patient follow-up and reproductive counseling to families could change considerably depending on the meaning of aCGH results.