Background:
Primary ciliary dyskinesia (PCD) is characterized by motile cilia dysfunction leading to chronic lung disease. PCD is inherited predominantly in an autosomal recessive manner, caused by pathogenic variants in more than 50 genes. 20-30% of patients with a well-defined PCD phenotype remain without molecular diagnosis, highlighting the need for periodic reanalysis. As variant interpretation has become the bottleneck in genetic diagnostics, we aim to investigate automated variant classification capability for the reanalysis of PCD patients.
Material and Methods:
A validation (n=30), a reanalysis (n=159), and a first-analysis cohort (n=66) of patients with a PCD phenotype were analyzed using the automated classification tool HerediClassify, screening 908 genes. Based on clinical records, phenotypic features were assessed using the PICADAR score (PrImary CiliARy DyskinesiA Rule) in children and the modified PICADAR score in adults. To all PICADAR-positive cases, the PP4 criterion for disease-specific phenotype was applied by HerediClassify.
Results:
HerediClassify showed a sensitivity of 0.67 and specificity of 1 in the validation cohort. In the reanalysis cohort, which included patients who had remained without a genetic diagnosis following routine diagnostic workup, six additional patients were genetically diagnosed. 71% of patients with a manual diagnosis were correctly identified by HerediClassify in the first-analysis cohort.
Conclusion:
Automated variant classification can enhance reanalysis efficiently by identifying high-priority variants. The systematic inclusion of phenotype-specific information, such as the PICADAR score, substantially improves the interpretation of genetic data and should be considered standard practice.

Co-authors: Paul Siek, Martin Wetzke, Felix C. Ringshausen, Ben Ole Staar, Gunnar Schmidt, Bernd Auber, Sandra V. Hardenberg