Introduction: Dilated cardiomyopathy (DCM) is a progressive disease of heart muscle with an incidence of approximately 0.57 per 100,000 children in the US. The spectrum and frequency of mutations in known DCM genes differ among adults, children, and infants.
Aim: To evaluate the diagnostic yield of genome-wide copy number variation (CNV) analysis and trio Whole Exome Sequencing (WES) with targeted analysis of genes implicated in paediatric DCM.
Methods: We identified 95 cases (from 85 families) diagnosed with DCM before 18 years of age. Thirteen families with a genetic diagnosis that was previously established by other sequencing techniques were excluded, and 41 families for other reasons. In 31 carefully phenotyped probands CNV-analysis (SNP-array) and trio-WES were performed. Human Phenotype Ontology (HPO)-terms were used for data filtering.
Results: A (likely) genetic diagnosis could be made in 14/31 families (45.2%). (Likely) pathogenic heterozygous variants were identified in TNNT2, SCN5A, TTN, MYH7 (4), MYL2 (2) and TPM1, and homozygous variants in SPEG (centronuclear myopathy) and GLB1 (GM1-gangliosidosis) using WES, as well as an 1p36.33p36.32 and an 10q25.2 deletion, using SNP-array. Compound heterozygous mutations in CEP135 (primary microcephaly) were identified in a child with syndromic DCM. Five patients carried autosomal recessive disease mutations that did not explain their phenotypes.
Conclusions: WES and CNV-analysis yielded diagnoses for 45% of our cohort. In 8/10 families ‘solved’ by WES a causal variant in a well-known DCM gene was identified. When CNV-analysis and WES would have been applied as primary tests to all patients, the potential yield could increase to approximately 51%. Combining CNV-analysis with trio-based WES, filtering for variants in a virtual, and flexible gene panel based on patient-specific HPO-terms, represent a comprehensive, personalized, cost- and time-efficient strategy to establish a diagnosis within the genetically highly heterogeneous paediatric DCM cohort. The latter technique provides the ability to stepwise analysis of a subset of genomic data, thereby minimizing the number of variants of unknown significance and preventing incidental findings in a proportion of patients.