Genetics and genotype-phenotype correlations in Finnish patients with dilated cardiomyopathy

Abstract

Aims Genetic analysis among patients with dilated cardiomyopathy (DCM) is becoming an important part of clinical assessment, as it is in hypertrophic cardiomyopathy (HCM). The genetics of DCM is complex and therefore next-generation sequencing strategies are essential when providing genetic diagnostics. To achieve maximum yield, the diagnostic approach should include comprehensive clinical phenotyping combined with high-quality, high-coverage deep sequencing of DCM-associated genes and clinical variant classification as a basis for defining true yield in genetic testing. Our study has combined a novel sequencing strategy and clinical interpretation to analyse the yield and genotype-phenotype correlations among well-phenotyped Finnish DCM patients.Despite our increased understanding of the genetic basis of dilated cardiomyopathy (DCM), the clinical utility and yield of clinically meaningful findings of comprehensive next-generation sequencing (NGS)-based genetic diagnostics in DCM has been poorly described. We utilized a high-quality oligonucleotide-selective sequencing (OS-Seq)-based targeted sequencing panel to investigate the genetic landscape of DCM in Finnish population and to evaluate the utility of OS-Seq technology as a novel comprehensive diagnostic tool. Methods and results Using OS-Seq, we targeted and sequenced the coding regions and splice junctions of 101 genes associated with cardiomyopathies in 145 unrelated Finnish patients with DCM. We developed effective bioinformatic variant filtering strategy and implemented strict variant classification scheme to reveal diagnostic yield and genotype-phenotype correlations. Implemented OS-Seq technology provided high coverage of the target region (median coverage 410x and 99.42% of the nucleotides were sequenced at least 15x read depth). Diagnostic yield was 35.2% (familial 47.6% and sporadic 25.6%, P = 0.004) when both pathogenic and likely pathogenic variants are considered as disease causing. Of these, 20 (53%) were titin (TTN) truncations (non-sense and frameshift) affecting all TTN transcripts. TTN truncations accounted for 20.6% and 14.6% of the familial and sporadic DCM cases, respectively. Conclusion Panel-based, high-quality NGS enables high diagnostic yield especially in the familial form of DCM, and bioinformatic variant filtering is a reliable step in the process of interpretation of genomic data in a clinical setting.