Why the study?
Despite the expanding role of genetics in cardiovascular medicine and increasing diagnostic success, unresolved challenges remain and about half of suspected Mendelian conditions still lack a precise molecular diagnosis.
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Design
Review
May limit NGS-driven precision medicine in inherited CV disease; leaves open needs for improved variant interpretation and novel diagnostics.
The diagnostic work-up of cardiovascular genetic diseases is emerging as a reference model for precision (cause and phenotype) and personalized medicine (tailored individual management). This approach is expanding across all areas of cardiovascular medicine, ranging from monogenic diseases to multifactorial disorders where 'risk factors' such as familial hypercholesterolaemia may play a role in the risk profile. In this context, cardiomyopathies provide an ideal reference because they are monogenic yet genetically heterogeneous diseases, much like many other cardiovascular genetic disorders. In this model, the genetic path starts with deep phenotyping of the patient and relatives and progresses with genetic testing including extensive multigene panels, up to whole-exome sequencing and whole-genome sequencing. Although genetic work-ups are increasingly successful, several unresolved challenges and limitations remain. These include the interpretation and reinterpretation of variants, as many pre-American College of Medical Genetics variants previously classified as likely pathogenic or pathogenic are now recognized as variants of uncertain significance or benign/likely benign; pathogenic variants missed with short-read next generation sequencing (NGS) technologies (e.g. deep intronic variants or Copy Number Variations); gene-specific issues such as pseudogenes and pseudo-exons; and differing interpretations of pathogenicity for the same gene defects by commercial pipelines. Despite widespread NGS-based testing, about half of suspected Mendelian conditions still lack a precise molecular diagnosis. New organizational models are needed to integrate emerging knowledge and innovations incorporating both clinical and genetic data into intelligent platforms that may support shared management pathways.
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Urtis et al. (2025) studied this question.
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