Renal cysts are clinically and genetically heterogeneous conditions. Autosomal dominant polycystic kidney disease (ADPKD) is the most frequent life-threatening genetic disease and mainly caused by mutations in PKD1. The presence of six PKD1 pseudogenes and tremendous allelic heterogeneity make molecular genetic testing challenging requiring laborious locus-specific amplification. Increasing evidence suggests a major role for PKD1 in early and severe cases of ADPKD and some patients with a recessive form. Furthermore it is becoming obvious that clinical manifestations can be mimicked by mutations in a number of other genes with the necessity for broader genetic testing. We established and validated a sequence capture based NGS testing approach for all genes known for cystic and polycystic kidney disease including PKD1. Thereby, we demonstrate that the applied standard mapping algorithm specifically aligns reads to the PKD1 locus and overcomes the complication of unspecific capture of pseudogenes. Employing careful and experienced assessment of NGS data, the method is shown to be very specific and equally sensitive as established methods. An additional advantage over conventional Sanger sequencing is the detection of copy number variations (CNVs). Sophisticated bioinformatic read simulation increased the high analytical depth of the validation study and further demonstrated the strength of the approach. We further raise some awareness of limitations and pitfalls of common NGS workflows when applied in complex regions like PKD1 demonstrating that quality of NGS needs more than high coverage of the target region. By this, we propose a time- and cost-efficient diagnostic strategy for comprehensive molecular genetic testing of polycystic kidney disease which is highly automatable and will be of particular value when therapeutic options for PKD emerge and genetic testing is needed for larger numbers of patients.