Multiomic analyses implicate a neurodevelopmental program in the pathogenesis of cerebral arachnoid cysts

Cerebral arachnoid cysts (ACs) are one of the most common and poorly understood types of developmental brain lesion. To begin to elucidate AC pathogenesis, we performed an integrated analysis of 617 patient-parent (trio) exomes, 152,898 human brain and mouse meningeal single-cell RNA sequencing transcriptomes and natural language processing data of patient medical records. We found that damaging de novo variants (DNVs) were highly enriched in patients with ACs compared with healthy individuals (P = 1.57 × 10-33). Seven genes harbored an exome-wide significant DNV burden. AC-associated genes were enriched for chromatin modifiers and converged in midgestational transcription networks essential for neural and meningeal development. Unsupervised clustering of patient phenotypes identified four AC subtypes and clinical severity correlated with the presence of a damaging DNV. These data provide insights into the coordinated regulation of brain and meningeal development and implicate epigenomic dysregulation due to DNVs in AC pathogenesis. Our results provide a preliminary indication that, in the appropriate clinical context, ACs may be considered radiographic harbingers of neurodevelopmental pathology warranting genetic testing and neurobehavioral follow-up. These data highlight the utility of a systems-level, multiomics approach to elucidate sporadic structural brain disease

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care