Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals.

Purpose: An early genetic diagnosis can guide the time-sensitive treatment of individuals with genetic epilepsies. However, most genetic diagnoses occur long after disease onset. We aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy through large-scale analysis of full-text electronic medical records.

Advances in big data and omics: Paving the way for discovery in childhood epilepsies.

The insights gained from big data and omics approaches have transformed the field of childhood genetic epilepsy. With an increasing number of individuals receiving genetic testing for seizures, we are provided with an opportunity to identify clinically relevant subgroups and extract meaningful observations from this large-scale clinical data. However, the volume of data from electronic medical records and omics (e.

The clinical and genetic spectrum of paediatric speech and language disorders in 52,143 individuals.

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52,143 individuals, reconstructing clinical histories using a large-scale data mining approach of the Electronic Medical Records (EMR) from an entire large paediatric healthcare network.

The role of common genetic variation in presumed monogenic epilepsies.

Background: The developmental and epileptic encephalopathies (DEEs) are the most severe group of epilepsies which co-present with developmental delay and intellectual disability (ID). DEEs usually occur in people without a family history of epilepsy and have emerged as primarily monogenic, with damaging rare mutations found in 50% of patients. Little is known about the genetic architecture of patients with DEEs in whom no pathogenic variant is identified.

Common risk variants for epilepsy are enriched in families previously targeted for rare monogenic variant discovery.

Background: The epilepsies are highly heritable conditions that commonly follow complex inheritance. While monogenic causes have been identified in rare familial epilepsies, most familial epilepsies remain unsolved. We aimed to determine (1) whether common genetic variation contributes to familial epilepsy risk, and (2) whether that genetic risk is enriched in familial compared with non-familial (sporadic) epilepsies.

Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery.

Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging.

Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data.

While genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.

Objective: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference.

Computational analysis of 10,860 phenotypic annotations in individuals with SCN2A-related disorders.

Purpose: Pathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype-phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed.

Methods: We extracted phenotypic information from primary descriptions of SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms.

Correction: A longitudinal footprint of genetic epilepsies using automated electronic medical record interpretation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Lessons learned from 40 novel PIGA patients and a review of the literature.

Objective: To define the phenotypic spectrum of phosphatidylinositol glycan class A protein (PIGA)-related congenital disorder of glycosylation (PIGA-CDG) and evaluate genotype-phenotype correlations.

Methods: Our cohort encompasses 40 affected males with a pathogenic PIGA variant. We performed a detailed phenotypic assessment, and in addition, we reviewed the available clinical data of 36 previously published cases and assessed the variant pathogenicity using bioinformatical approaches.

A Recurrent Missense Variant in AP2M1 Impairs Clathrin-Mediated Endocytosis and Causes Developmental and Epileptic Encephalopathy.

The developmental and epileptic encephalopathies (DEEs) are heterogeneous disorders with a strong genetic contribution, but the underlying genetic etiology remains unknown in a significant proportion of individuals. To explore whether statistical support for genetic etiologies can be generated on the basis of phenotypic features, we analyzed whole-exome sequencing data and phenotypic similarities by using Human Phenotype Ontology (HPO) in 314 individuals with DEEs. We identified a de novo c.