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Reelin (RELN) is a secreted glycoprotein essential for cerebral cortex development. In humans, recessive RELN variants cause cortical and cerebellar malformations, while heterozygous variants were associated with epilepsy, autism, and mild cortical abnormalities. However, the functional effects of RELN variants remain unknown. We identified inherited and de novo RELN missense variants in heterozygous patients with neuronal migration disorders (NMDs) as diverse as pachygyria and polymicrogyria. We investigated in culture and in the developing mouse cerebral cortex how different variants impacted RELN function. Polymicrogyria-associated variants behaved as gain-of-function, showing an enhanced ability to induce neuronal aggregation, while those linked to pachygyria behaved as loss-of-function, leading to defective neuronal aggregation/migration. The pachygyria-associated de novo heterozygous RELN variants acted as dominant-negative by preventing WT RELN secretion in culture, animal models, and patients, thereby causing dominant NMDs. We demonstrated how mutant RELN proteins in vitro and in vivo predict cortical malformation phenotypes, providing valuable insights into the pathogenesis of such disorders., Introduction The neocortex is composed of 6 layers that are built during embryonic development through highly orchestrated processes of successive generation of cohorts of glutamatergic neurons in the proliferative zones [...]

Background: The majority of genetic epilepsies remain unsolved in terms of specific genotype. Phenotype‐based genomic analyses have shown potential to strengthen genomic analysis in various ways, including improving analytical efficacy. Methods: We have tested a standardised phenotyping method termed 'Phenomodels' for integrating deep‐phenotyping information with our in‐house developed clinical whole exome/genome sequencing analytical pipeline. Phenomodels includes a user‐friendly epilepsy phenotyping template and an objective measure for selecting which template terms to include in individualised Human Phenotype Ontology (HPO) gene panels. In a pilot study of 38 previously solved cases of developmental and epileptic encephalopathies, we compared the sensitivity and specificity of the individualised HPO gene panels with the clinical epilepsy gene panel. Results: The Phenomodels template showed high sensitivity for capturing relevant phenotypic information, where 37/38 individuals' HPO gene panels included the causative gene. The HPO gene panels also had far fewer variants to assess than the epilepsy gene panel. Conclusion: We have demonstrated a viable approach for incorporating standardised phenotype information into clinical genomic analyses, which may enable more efficient analysis. [ABSTRACT FROM AUTHOR]