The autosomal recessive Congenital Muscular Dystrophies associated with a merosin deficiency, also known as type 1A muscular dystrophy (MDC1A), results from mutations of the LAMA2 gene. This large gene, composed of 65 exons, encodes the a2-chain subunit of laminin-2 (merosin). For many years, identification of the lack of expression of merosin on muscle biopsies was considered to initiate a molecular analysis by conventional Sanger sequencing. In the majority of cases (80%), the two pathogenic mutations were directly identified by this approach. Nevertheless, for the remaining cases with a single or no mutation, complementary approaches were necessary. They include the detection of large rearrangement; either by MLPA or CGH-array and the search for deep intronic mutations impacting splicing through the mRNA analysis. Altogether, these approaches allowed for the identification of disease-causing mutations in more than 95% of families. Since these early days, patients with partial loss of merosin have been identified. This has paved the way to the identification of a wide range of phenotypes associated with mutations of the LAMA2 gene, ranging from the most severe forms in patients with hypotonia at birth or in the first weeks of life, to rare patients achieving independent ambulation. Most patients, among those with partial laminin-a2 deficiency, belong to this last category. The identification of those milder phenotypes have also led to higher difficulty to select which genes have to be analyzed based on the clinical presentation. This led to the development of NGS gene panels in order to simultaneously screen all genes reported to be associated with congenital muscular dystrophies. Thus, in a single NGS experiment, it is now possible to screen the 19 most commonly involved genes and thus limit the diagnostic wandering. This new technology, while being able to rapidly deliver the sequence of all genes, also place the geneticist in front of multiple candidate mutations, which need to be evaluated prior to reporting the identification of the disease-causing mutation(s) in a family. Not considering the need for large data storage capacity, it is now mandatory to have access to efficient bioinformatics systems and to trained geneticists. In this presentation, I will demonstrate the benefits and limits of this approach for the diagnosis of DMC.


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