A nomenclature and classification for the congenital myasthenic syndromes: preparing for FAIR data in the genomic era

Abstract

BackgroundCongenital myasthenic syndromes (CMS) are a heterogeneous group of inherited neuromuscular disorders sharing the common feature of fatigable weakness due to defective neuromuscular transmission. Despite rapidly increasing knowledge about the genetic origins, specific features and potential treatments for the known CMS entities, the lack of standardized classification at the most granular level has hindered the implementation of computer-based systems for knowledge capture and reuse. Where individual clinical or genetic entities do not exist in disease coding systems, they are often invisible in clinical records and inadequately annotated in information systems, and features that apply to one disease but not another cannot be adequately differentiated.ResultsWe created a detailed classification of all CMS disease entities suitable for use in clinical and genetic databases and decision support systems. To avoid conflict with existing coding systems as well as with expert-defined group-level classifications, we developed a collaboration with the Orphanet nomenclature for rare diseases, creating a clinically understandable name for each entity and placing it within a logical hierarchy that paves the way towards computer-aided clinical systems and improved knowledge bases for CMS that can adequately differentiate between types and ascribe relevant expert knowledge to each.ConclusionsWe suggest that data science approaches can be used effectively in the clinical domain in a way that does not disrupt preexisting expert classification and that enhances the utility of existing coding systems. Our classification provides a comprehensive view of the individual CMS entities in a manner that supports differential diagnosis and understanding of the range and heterogeneity of the disease but that also enables robust computational coding and hierarchy for machine-readability. It can be extended as required in the light of future scientific advances, but already provides the starting point for the creation of FAIR (Findable, Accessible, Interoperable and Reusable) knowledge bases of data on the congenital myasthenic syndromes.

Targeted therapies for congenital myasthenic syndromes: systematic review and steps towards a treatabolome

“…We considered evidence dealing with adults and children with a genetic diagnosis of CMS or one of its subtypes, adopting a broad definition of CMS as any genetic neuromuscular condition manifesting with fatigable weakness of skeletal muscle and apparent NMJ involvement [18]. We included participants with any level of severity, age of onset and level of penetrance of phenotypic features, provided the underlying genetic defect gave rise to an apparent CMS.…”

Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder

“…From the cases available within the study PI's cohort in the RD‐Connect GPAP, we selected a restricted cohort of 29 individuals with a confirmed genetic diagnosis (“solved cases”), all of which had a similar phenotypic profile and homozygous causative variant. All individuals had originally been clinically suspected of a congenital myasthenic syndrome (CMS; Thompson et al, ) based on their phenotypic presentation to the submitting clinician and had been submitted within the RD‐Connect PhenoTips instance as CMS cases. All were subsequently diagnosed with a homozygous recessive cause of disease that was confirmed by further analysis by the submitting center.…”

Validating gene-phenotype associations using relationships in the UMLS

“…In some cases, a disease name listed in a genetic database will directly match an ICD code description, but frequently the disease name does not directly correspond to an ICD code. These issues are particularly challenging in rare diseases, where ICD codes may not exist or, more often, may not be entered into the EHR with a sufficient level of granularity to distinguish among different diseases [10]. A manual approach to mapping diseases to ICD codes for thousands of genes would be very time-consuming, and a textsimilarity approach would miss diseases where the appropriate ICD code description is unlike the disease name.…”