Large Cohort Integrative Multiomic Analyses Identifies Novel Risk Genes for Congenital Cerebral Ventriculomegaly and Characterizes Distinct Spatiotemporal Clusters of Pathogenesis
Garrett Allington1, Kedous Mekbib2, Evan Dennis3, Danielle Miyagishima4, Kristopher Kahle5
1Neurology, Columbia University, 2Neurosurgery, Mayo Clinic, 3Neurosurgery, Harvard University, 4Yale University, 5Neurosurgery, Massachusetts General Hospital
Objective:
To utilize an integrative multiomic approach to uncover novel genetic etiologies and pathomechanisms underlying congenital cerebral ventriculomegaly (CCV) and to explore its overlap with neurodevelopmental delay (NDD) disorders.
Background:
Timely diagnosis of NDD remains challenging, despite increasing prevalence and improved outcomes with early intervention. CCV, the most frequently diagnosed prenatal brain abnormality, is associated with NDD and other congenital neuropsychiatric conditions through unclear mechanisms, underscoring the need for deeper pathomechanistic insights.
Design/Methods:
We conducted an integrative analysis of 2,796 CCV proband-parent (trio) exomes, 1,211,359 single-cell transcriptomes, protein-protein interaction networks, and natural language processing-derived data from CCV patient medical records.
Results:
Our findings revealed a significant enrichment of damaging de novo variants (DNVs) in CCV probands compared to 1,798 healthy control trios (Adj.P=3.03×10−122). Thirty-five genes exhibited exome-wide significant DNV burden, of which thirty-one were newly implicated in CCV. We also identified 98 high-pLI genes with multiple damaging DNVs. Unbiased comparison of experimentally validated CCV genes against DisGeNET-curated disease gene lists demonstrated strong overlap with NDD-associated genes (Adj.P=6.23×10−64). Spatiotemporal transcriptomic analysis identified five distinct gene expression clusters, with the largest cluster localized to early-gestational neuroepithelial progenitor cells in the telencephalic vesicle, and the second-largest cluster centered around mid-gestational periventricular neocortical intermediate progenitor cells. Phenotypic analysis using artificial intelligence revealed distinct clinical profiles among proband gene clusters.
Conclusions:
This study provides new insights into the genetic architecture of ventricular development and cortical patterning, identifies novel CCV-associated genes, and suggests specific phenotypic profiles based on gene cluster disruption. These findings highlight the potential utility of genetic testing in patients with CCV, both to enable earlier diagnosis of NDDs and to guide characterization of phenotypic presentation based on disrupted gene clusters. CCV may serve as a prenatal sonographic risk factor for NDDs, warranting further exploration through clinical genetic testing.
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