Presented by

Dr. Christophe Béroud

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 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 opened the way to the identification of a wide range of

phenotypes associated to mutations of the LAMA2 gene, ranging from the most severe forms

with patients presenting 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 also led to a 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.