# Phenotype-Driven Next-Generation Sequencing and Structure-Based In Silico Analysis Reveal Disease-Specific Diagnostic Yield and Genotype–Phenotype Correlations in Inherited Kidney Diseases

## TL;DR

This study shows how using genetic testing and computer analysis helps diagnose inherited kidney diseases and understand how genes relate to symptoms.

## Contribution

The study demonstrates disease-specific diagnostic yield and genotype–phenotype correlations using phenotype-driven NGS and in silico analysis.

## Key findings

- Alport syndrome had the highest diagnostic yield (71.4%) with variants in COL4A3, COL4A4, and COL4A5.
- PKD had a lower yield (41.0%) with PKD1 as the main gene involved, but many cases remained genetically negative.
- Variants of uncertain significance were found in critical protein domains, suggesting potential pathogenic roles.

## Abstract

Background: Inherited kidney diseases represent a genetically and clinically heterogeneous group of disorders affecting both pediatric and adult populations. Advances in next-generation sequencing (NGS) have improved diagnostic precision; however, genotype–phenotype correlations and diagnostic yield vary substantially across disease entities. Methods:We retrospectively evaluated 165 patients referred for genetic testing due to suspected inherited kidney disease. Patients were classified into three clinical groups: polycystic kidney disease, Alport syndrome, and other syndromic patients with inherited kidney diseases. Genetic analysis was performed using NGS with Human Phenotype Ontology–based gene filtering and included evaluation of both single-nucleotide variants and copy number variations. Results: Overall diagnostic yield differed markedly between groups. A molecular diagnosis was achieved in 71.4% of Alport patients, 41.0% of PKD patients, and 70.2% of patients in the Other syndromic group. In the Alport group, variants were identified exclusively in COL4A3, COL4A4, and COL4A5, with pathogenicity and gene involvement correlating with disease severity and the presence of extrarenal manifestations. The PKD group showed predominant involvement of PKD1, followed by PKHD1 and PKD2, while a substantial proportion of patients remained genetically negative, reflecting technical and biological complexity. The Other group exhibited pronounced genetic heterogeneity, with variants distributed across multiple genes involved in tubular, glomerular, metabolic, and ciliopathy-related pathways. Computational assessments demonstrated that several variants of uncertain significance (VUS) were located in functionally critical domains and were predicted to disrupt protein stability, intermolecular interactions, or conserved structural motifs, thereby supporting the biological plausibility of their potential pathogenic impact. Conclusions: Phenotype-driven NGS enables effective molecular diagnosis across diverse inherited kidney diseases while revealing disease-specific differences in diagnostic yield and genotype–phenotype correlations. Systematic inclusion of variants of uncertain significance and careful integration of genetic and clinical data are essential for accurate interpretation and long-term patient management. Collectively, this study enhances understanding of inherited kidney diseases and underscores the value of integrating comprehensive genomic and computational approaches into routine nephrogenetic practice.