Phenotype-driven Genomics Enhance Diagnosis in Children with Unresolved Neuromuscular Diseases – European Journal of Human Genetics
In this study, we demonstrate how the integration of standardized deep phenotyping with genomic techniques has significantly improved diagnostic rates for children suffering from unresolved childhood-onset neuromuscular diseases (NMDs) who had undetermined results from previous exome studies.
We focused on 58 individuals within the framework of the Solve-RD Project, a European Commission-funded initiative that employs combined genome and RNA sequencing approaches for individuals with rare genetic diseases lacking a definitive genetic diagnosis despite extensive prior exome sequencing. This collaborative effort includes partners such as the Neuromuscular Unit of Hospital Sant Joan de Déu and the Centro Nacional de Análisis Genómico (CNAG) in Barcelona.
Following established procedures by Hospital Sant Joan de Déu, findings were reported to affected families, which included generating a genetic report and offering appropriate genetic counseling.
Criteria and Cohort Selection
We selected individuals with genetically unresolved NMDs who were receiving care at the Neuromuscular Unit of Hospital Sant Joan de Déu. The inclusion criteria were twofold: (1) the neuromuscular disease likely had a genetic origin and (2) previous clinical or exome sequencing (CES or WES) had been completed without yielding a genetic diagnosis. The initial CES/WES had an average coverage of over 100x and minimum coverage of at least 95% in target regions.
Deep Phenotyping
All affected individuals underwent deep phenotyping, involving comprehensive clinical examination and the collection of histopathological, neurophysiological, laboratory, and MRI muscle imaging data from electronic medical records. Phenotypic features were pseudonymized and captured using standardized Human Phenotype Ontology (HPO) terms in RD-Connect PhenoStore.
Complementary tests included electromyogram/electroneurogram, muscle MRI, muscle biopsy, and serum creatine kinase levels. Based on this comprehensive assessment, each individual was clinically categorized into one of five disease categories.
Genomic Data Analysis
Blood samples were collected from 58 families and processed using standard procedures. Genome sequencing of the index case, parents, and affected siblings was performed on a BGI DNBSEQ-G400 Platform. Genomic data were analyzed using the RD-Connect standard analysis pipeline, mapping sequencing reads to the human genome and identifying single-nucleotide variants (SNVs), short insertions and deletions, structural variants (SVs), and copy number variants (CNVs).
The multi-step filtering strategy prioritized candidate variants by considering allele frequency, functional impact predictions, and trio segregation information. The Muscle Gene Table was initially consulted to focus on variants in genes associated with NMDs, followed by an unrestricted analysis of all variants.
Deep phenotyping informed the hypothesis for each individual’s neuromuscular disease type (e.g., neuropathy, myasthenia, myopathy) and guided variant assessment while avoiding premature dismissal. Analysts also considered the functional protein data and its expression in peripheral nerve and muscle.
Multidisciplinary Approach and Functional Studies
A multidisciplinary team reviewed findings, enhancing variant interpretation and prioritization. Establishing a continuous dialogue between geneticists and clinicians ensured precise phenotyping was revisited during the prioritization process. Functional studies further facilitated variant reclassification, both for variants of uncertain significance (VUS) within compatible phenotypes and for genes not associated with any human condition but with a hint of potential causality.
RNA Sequencing
RNA was extracted from muscle tissue in 13 affected individuals and processed using standard extraction methods. RNA-Seq libraries were generated and sequenced to identify aberrant expression and splicing events using the DROP v1.3.3 pipeline. The detected outliers were further inspected to assess their compatibility with the known causes of NMDs.
Significant Findings and Impact
Through this extensive and comprehensive approach combining genomics and precise phenotyping, we identified diagnostic variants in a substantial portion of the studied cohort. This provides affected families with definitive genetic diagnoses and enables more personalized management and treatment plans.
The study underscores the critical importance of integrating deep phenotyping with advanced genomic techniques to enhance diagnostic yields in unresolved neuromuscular diseases. By leveraging collaborative efforts and innovative technologies, we pave the way for improved clinical outcomes for children with rare genetic disorders.
Overall, the study offers valuable insights and sets a precedent for future research and clinical practices in the field of neuromuscular genetics, ensuring that more patients receive accurate and comprehensive diagnoses.
