Genome Medicine (Dec 2022)
The role of genetic testing in diagnosis and care of inherited cardiac conditions in a specialised multidisciplinary clinic
Abstract Background The diagnostic yield of genetic testing for inherited cardiac diseases is up to 40% and is primarily indicated for screening of at-risk relatives. Here, we evaluate the role of genomics in diagnosis and management among consecutive individuals attending a specialised clinic and identify those with the highest likelihood of having a monogenic disease. Methods A retrospective audit of 1697 consecutive, unrelated probands referred to a specialised, multidisciplinary clinic between 2002 and 2020 was performed. A concordant clinical and genetic diagnosis was considered solved. Cases were classified as likely monogenic based on a score comprising a positive family history, young age at onset, and severe phenotype, whereas low-scoring cases were considered to have a likely complex aetiology. The impact of a genetic diagnosis was evaluated. Results A total of 888 probands fulfilled the inclusion criteria, and genetic testing identified likely pathogenic or pathogenic (LP/P) variants in 330 individuals (37%) and suspicious variants of uncertain significance (VUS) in 73 (8%). Research-focused efforts identified 46 (5%) variants, missed by conventional genetic testing. Where a variant was identified, this changed or clarified the final diagnosis in a clinically useful way for 51 (13%). The yield of suspicious VUS across ancestry groups ranged from 15 to 20%, compared to only 10% among Europeans. Even when the clinical diagnosis was uncertain, those with the most monogenic disease features had the greatest diagnostic yield from genetic testing. Conclusions Research-focused efforts can increase the diagnostic yield by up to 5%. Where a variant is identified, this will have clinical utility beyond family screening in 13%. We demonstrate the value of genomics in reaching an overall diagnosis and highlight inequities based on ancestry. Acknowledging our incomplete understanding of disease phenotypes, we propose a framework for prioritising likely monogenic cases to solve their underlying cause of disease.