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GRIN2B encephalopathy: novel findings on phenotype, variant clustering, functional consequences and treatment aspects.

ABSTRACT: BACKGROUND:We aimed for a comprehensive delineation of genetic, functional and phenotypic aspects of GRIN2B encephalopathy and explored potential prospects of personalised medicine. METHODS:Data of 48 individuals with de novo GRIN2B variants were collected from several diagnostic and research cohorts, as well as from 43 patients from the literature. Functional consequences and response to memantine treatment were investigated in vitro and eventually translated into patient care. RESULTS:Overall, de novo variants in 86 patients were classified as pathogenic/likely pathogenic. Patients presented with neurodevelopmental disorders and a spectrum of hypotonia, movement disorder, cortical visual impairment, cerebral volume loss and epilepsy. Six patients presented with a consistent malformation of cortical development (MCD) intermediate between tubulinopathies and polymicrogyria. Missense variants cluster in transmembrane segments and ligand-binding sites. Functional consequences of variants were diverse, revealing various potential gain-of-function and loss-of-function mechanisms and a retained sensitivity to the use-dependent blocker memantine. However, an objectifiable beneficial treatment response in the respective patients still remains to be demonstrated. CONCLUSIONS:In addition to previously known features of intellectual disability, epilepsy and autism, we found evidence that GRIN2B encephalopathy is also frequently associated with movement disorder, cortical visual impairment and MCD revealing novel phenotypic consequences of channelopathies.

SUBMITTER: Platzer K

PROVIDER: S-EPMC5656050 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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<i>GRIN2B</i> encephalopathy: novel findings on phenotype, variant clustering, functional consequences and treatment aspects.

Platzer Konrad K Yuan Hongjie H Schütz Hannah H Winschel Alexander A Chen Wenjuan W Hu Chun C Kusumoto Hirofumi H Heyne Henrike O HO Helbig Katherine L KL Tang Sha S Willing Marcia C MC Tinkle Brad T BT Adams Darius J DJ Depienne Christel C Keren Boris B Mignot Cyril C Frengen Eirik E Strømme Petter P Biskup Saskia S Döcker Dennis D Strom Tim M TM Mefford Heather C HC Myers Candace T CT Muir Alison M AM LaCroix Amy A Sadleir Lynette L Scheffer Ingrid E IE Brilstra Eva E van Haelst Mieke M MM van der Smagt Jasper J JJ Bok Levinus A LA Møller Rikke S RS Jensen Uffe B UB Millichap John J JJ Berg Anne T AT Goldberg Ethan M EM De Bie Isabelle I Fox Stephanie S Major Philippe P Jones Julie R JR Zackai Elaine H EH Abou Jamra Rami R Rolfs Arndt A Leventer Richard J RJ Lawson John A JA Roscioli Tony T Jansen Floor E FE Ranza Emmanuelle E Korff Christian M CM Lehesjoki Anna-Elina AE Courage Carolina C Linnankivi Tarja T Smith Douglas R DR Stanley Christine C Mintz Mark M McKnight Dianalee D Decker Amy A Tan Wen-Hann WH Tarnopolsky Mark A MA Brady Lauren I LI Wolff Markus M Dondit Lutz L Pedro Helio F HF Parisotto Sarah E SE Jones Kelly L KL Patel Anup D AD Franz David N DN Vanzo Rena R Marco Elysa E Ranells Judith D JD Di Donato Nataliya N Dobyns William B WB Laube Bodo B Traynelis Stephen F SF Lemke Johannes R JR

Journal of medical genetics 20170404 7

<h4>Background</h4>We aimed for a comprehensive delineation of genetic, functional and phenotypic aspects of <i>GRIN2B</i> encephalopathy and explored potential prospects of personalised medicine.<h4>Methods</h4>Data of 48 individuals with de novo <i>GRIN2B</i> variants were collected from several diagnostic and research cohorts, as well as from 43 patients from the literature. Functional consequences and response to memantine treatment were investigated in vitro and eventually translated into p ...[more]

PMID: 28377535

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Project description:BackgroundClustering of observations is a common phenomenon in epidemiological and clinical research. Previous studies have highlighted the importance of using multilevel analysis to account for such clustering, but in practice, methods ignoring clustering are often employed. We used simulated data to explore the circumstances in which failure to account for clustering in linear regression could lead to importantly erroneous conclusions.MethodsWe simulated data following the random-intercept model specification under different scenarios of clustering of a continuous outcome and a single continuous or binary explanatory variable. We fitted random-intercept (RI) and ordinary least squares (OLS) models and compared effect estimates with the "true" value that had been used in simulation. We also assessed the relative precision of effect estimates, and explored the extent to which coverage by 95% confidence intervals and Type I error rates were appropriate.ResultsWe found that effect estimates from both types of regression model were on average unbiased. However, deviations from the "true" value were greater when the outcome variable was more clustered. For a continuous explanatory variable, they tended also to be greater for the OLS than the RI model, and when the explanatory variable was less clustered. The precision of effect estimates from the OLS model was overestimated when the explanatory variable varied more between than within clusters, and was somewhat underestimated when the explanatory variable was less clustered. The cluster-unadjusted model gave poor coverage rates by 95% confidence intervals and high Type I error rates when the explanatory variable was continuous. With a binary explanatory variable, coverage rates by 95% confidence intervals and Type I error rates deviated from nominal values when the outcome variable was more clustered, but the direction of the deviation varied according to the overall prevalence of the explanatory variable, and the extent to which it was clustered.ConclusionsIn this study we identified circumstances in which application of an OLS regression model to clustered data is more likely to mislead statistical inference. The potential for error is greatest when the explanatory variable is continuous, and the outcome variable more clustered (intraclass correlation coefficient is ≥ 0.01).

Dataset Information

GRIN2B encephalopathy: novel findings on phenotype, variant clustering, functional consequences and treatment aspects.

Publications

<i>GRIN2B</i> encephalopathy: novel findings on phenotype, variant clustering, functional consequences and treatment aspects.

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