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Identification of LAMA1 mutations ends diagnostic odyssey and has prognostic implications for patients with presumed Joubert syndrome

Authors :
Sarah Wedderburn
Miguel Barroso-Gil
Laura Powell
Lorraine Cowley
Jill Clayton-Smith
John A. Sayer
Eric Olinger
Ian D. Wilson
Marta Bertoli
Eugen Boltshauser
Simon C Ramsden
Tara Montgomery
Sarah J. Rice
Usha Kini
Elizabeth Harris
Vijayalakshmi Salem Ramakumaran
Sally Johnson
Source :
Brain Communications. 3
Publication Year :
2021
Publisher :
Oxford University Press (OUP), 2021.

Abstract

Paediatric neurology syndromes are a broad and complex group of conditions with a large spectrum of clinical phenotypes. Joubert syndrome is a genetically heterogeneous neurological ciliopathy syndrome with molar tooth sign as the neuroimaging hallmark. We reviewed the clinical, radiological and genetic data for several families with a clinical diagnosis of Joubert syndrome but negative genetic analysis. We detected biallelic pathogenic variants in LAMA1, including novel alleles, in each of the four cases we report, thereby establishing a firm diagnosis of Poretti–Boltshauser syndrome. Analysis of brain MRI revealed cerebellar dysplasia and cerebellar cysts, associated with Poretti–Boltshauser syndrome and the absence of typical molar tooth signs. Using large UK patient cohorts, the relative prevalence of Joubert syndrome as a cause of intellectual disability was 0.2% and of Poretti–Boltshauser syndrome was 0.02%. We conclude that children with congenital brain disorders that mimic Joubert syndrome may have a delayed diagnosis due to poor recognition of key features on brain imaging and the lack of inclusion of LAMA1 on molecular genetic gene panels. We advocate the inclusion of LAMA1 genetic analysis on all intellectual disability and Joubert syndrome gene panels and promote a wider awareness of the clinical and radiological features of these syndromes.

Details

ISSN :
26321297
Volume :
3
Database :
OpenAIRE
Journal :
Brain Communications
Accession number :
edsair.doi...........719f50fc5b79f896741b31a28c7f76b8