P.98 Calculating the genetic prevalence of congenital myasthenic syndromes based on data from genomic databases

Abstract

Congenital myasthenic syndromes (CMS) are a heterogeneous group of rare inherited neuromuscular disorders characterized by fatigable weakness owing to compromised function of the neuromuscular junction. Next-generation sequencing has dramatically increased the number of genetic defects reported as causative of CMS, with over 30 genes implicated. Although all CMS subtypes share common features, their clinical and genetic heterogeneity, as well as referral and ascertainment biases in diagnostic practice, have made phenotype-based prevalence estimations difficult, with publications in the last decade providing estimates ranging from 1.8 cases per million total population to 22.2 cases per million children. This lack of accurate data hampers diagnosis, healthcare provision and therapy development. Most CMS subtypes are recessive, meaning that carriers of single variants that are pathogenic when present in homozygosity or compound heterozygosity can be found in the healthy general population. The new large-scale population databases of genomic data that have come online in recent years thus provide the opportunity to calculate the genetic prevalence or lifetime risk of autosomal recessive CMS based on the allele frequency of individual variants in an unaffected population. Based on reported pathogenic/likely pathogenic variants in databases including ClinVar, LOVD, HGMD and RD-Connect and predicted pathogenic variants from in silico predictions, we established a comprehensive curated list of known and likely pathogenic variants across 31 disease genes. We then queried the Genome Aggregation Database (gnomAD), the UK Biobank WES Cohort and the RD-Connect Genome-Phenome Analysis Platform to establish the allele frequency of disease-causing variants in genes associated with autosomal recessive congenital myasthenic syndromes. Based on this, we provide a population-genetic estimation of the genetic prevalence of CMS and compare it to existing published estimates. This data may be used to assign diagnostic probabilities, provide insights into numbers of patients amenable to clinical research and novel therapies, and aid resource allocation in therapy development and healthcare provision.