Frontiers in Genetics (Mar 2022)
Whole Exome Sequencing in 16p13.11 Microdeletion Patients Reveals New Variants Through Deductive and Systems Medicine Approaches
Abstract
The 16p13.11 microdeletion, whose prevalence in the general population is about 0.04%, is known in literature as a predisposition factor to neurodevelopmental disorders, being found in about 0.13% of patients with schizophrenia, in 0.5–0.6% of patient with epilepsy, cognitive impairment, autism spectrum disorder (ASD) and aggressiveness. The goal of this study was to identify a specific gene set pattern unique for the affected patients in comparison with other familial components. Due to the incomplete penetrance of this copy number variant (CNV), we studied by whole exome sequencing (WES), with particular regard of 850 SFARI genes, three families with an affected member carrier of inherited 16p13.11 and 16p13.11p12.3 microdeletion and one family with an affected member with a de novo 16p13.11 microdeletion. By combining a deductive approach together with personalized network models, we identified gene signatures potentially capable of explaining the clinical phenotype. Candidate variants in genes of interest were identified as possibly involved in determining the neurological phenotype of the four patients, such as compound heterozygosity in CECR2, variants in MTOR and RICTOR genes, compound heterozygous single nucleotide variants in the LRRK2 gene. Moreover, genes present in the microdeletion region were partially present as central nodes, with a focus on NDE1. No additional pathogenetic or uncertain CNVs were found in all four patients. No significant variants were detected in genes included in the microdeletion in patients 1, 2 and 3, excluding the finding of unmasked recessive variants. In conclusion, WES is a fundamental tool in the genetic investigation of patients having a predisposing variant, which is not sufficient to define the clinical phenotype. Moreover, the analysis of WES data using Systems medicine tools, such as personalized network models, led to the prioritization of genes on a high throughput scale and to discover variants in genes that were not prioritized at first.
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