Search

OBJECTIVE: We sought to identify novel genes and to establish the contribution of known genes in a large cohort of patients with nonsyndromic sporadic polymicrogyria and epilepsy. METHODS: We enrolled participants with polymicrogyria and their parents through the Epilepsy Phenome/Genome Project. We performed phenotyping and whole exome sequencing (WES), trio analysis, and gene-level collapsing analysis to identify de novo or inherited variants, including germline or mosaic (postzygotic) single nucleotide variants, small insertion-deletion (indel) variants, and copy number variants present in leukocyte-derived DNA. RESULTS: Across the cohort of 86 individuals with polymicrogyria and epilepsy, we identified seven with pathogenic or likely pathogenic variants in PIK3R2, including four germline and three mosaic variants. PIK3R2 was the only gene harboring more than expected de novo variants across the entire cohort, and likewise the only gene that passed the genome-wide threshold of significance in the gene-level rare variant collapsing analysis. Consistent with previous reports, the PIK3R2 phenotype consisted of bilateral polymicrogyria concentrated in the perisylvian region with macrocephaly. Beyond PIK3R2, we also identified one case each with likely causal de novo variants in CCND2 and DYNC1H1 and biallelic variants in WDR62, all genes previously associated with polymicrogyria. Candidate genetic explanations in this cohort included single nucleotide de novo variants in other epilepsy-associated and neurodevelopmental disease-associated genes (SCN2A in two individuals, GRIA3, CACNA1C) and a 597-kb deletion at 15q25, a neurodevelopmental disease susceptibility locus. SIGNIFICANCE: This study confirms germline and postzygotically acquired de novo variants in PIK3R2 as an important cause of bilateral perisylvian polymicrogyria, notably with macrocephaly. In total, trio-based WES identified a genetic diagnosis in 12% and a candidate diagnosis in 6% of our polymicrogyria

OBJECTIVE: Classification of epilepsy into types and subtypes is important for both clinical care and research into underlying disease mechanisms. A quantitative, data-driven approach may augment traditional electroclinical classification and shed new light on existing classification frameworks. METHODS: We used latent class analysis, a statistical method that assigns subjects into groups called latent classes based on phenotypic elements, to classify individuals with common familial epilepsies from the Epi4K Multiplex Families study. Phenotypic elements included seizure types, seizure symptoms, and other elements of the medical history. We compared class assignments to traditional electroclinical classifications and assessed familial aggregation of latent classes. RESULTS: A total of 1120 subjects with epilepsy were assigned to five latent classes. Classes 1 and 2 contained subjects with generalized epilepsy, largely reflecting the distinction between absence epilepsies and younger onset (class 1) versus myoclonic epilepsies and older onset (class 2). Classes 3 and 4 contained subjects with focal epilepsies, and in contrast to classes 1 and 2, these did not adhere as closely to clinically defined focal epilepsy subtypes. Class 5 contained nearly all subjects with febrile seizures plus or unknown epilepsy type, as well as a few subjects with generalized epilepsy and a few with focal epilepsy. Family concordance of latent classes was similar to or greater than concordance of clinically defined epilepsy types. SIGNIFICANCE: Quantitative classification of epilepsy has the potential to augment traditional electroclinical classification by (1) combining some syndromes into a single class, (2) splitting some syndromes into different classes, (3) helping to classify subjects who could not be classified clinically, and (4) defining the boundaries of clinically defined classifications. This approach can guide future research, including molecular genetic studies, by identifyi

BACKGROUND AND PURPOSE: In this study, we used power analysis to calculate required sample sizes to detect group-level changes in quantitative neuroanatomical estimates derived from MRI scans obtained from multiple imaging centers. Sample size estimates were derived from (i) standardized 3T image acquisition protocols and (ii) nonstandardized clinically acquired images obtained at both 1.5 and 3T as part of the multicenter Human Epilepsy Project. Sample size estimates were compared to assess the benefit of standardizing acquisition protocols. METHODS: Cortical thickness, hippocampal volume, and whole brain volume were estimated from whole brain T1-weighted MRI scans processed using Freesurfer v6.0. Sample sizes required to detect a range of effect sizes were calculated using (i) standard t-test based power analysis methods and (ii) a nonparametric bootstrap approach. RESULTS: A total of 32 participants were included in our analyses, aged 29.9 ± 12.62 years. Standard deviation estimates were lower for all quantitative neuroanatomical metrics when assessed using standardized protocols. Required sample sizes per group to detect a given effect size were markedly reduced when using standardized protocols, particularly for cortical thickness changes <.2 mm and hippocampal volume changes <10%. CONCLUSIONS: The use of standardized protocols yielded up to a five-fold reduction in required sample sizes to detect disease-related neuroanatomical changes, and is particularly beneficial for detecting subtle effects. Standardizing image acquisition protocols across scanners prior to commencing a study is a valuable approach to increase the statistical power of multicenter MRI studies.