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Braunisch, M.C.* ; Großewinkelmann, C.M.* ; Menke, M.* ; Hannane, N.* ; Berutti, R.* ; Ćomić, J.* ; Günthner, R.* ; Renders, L.* ; Schmaderer, C.* ; Heemann, U.* ; Riedhammer, K.M.* ; Wagner, M. ; Hoefele, J.*

Estimating lifetime risk of autosomal recessive kidney diseases using population-based genotypic data.

Kidney Int. Rep. 10, 2384-2393 (2025)

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Introduction: Monogenic kidney diseases, though rare, exhibit a wide spectrum of clinical manifestations. The clinical and genetic diversity and potential biases in patient referrals and identification present challenges in accurately estimating prevalences based solely on phenotype. Our aim was to determine the calculated lifetime risk associated with autosomal recessive kidney diseases (ARKDs) using population-based genotype data. Methods: We conducted a comprehensive literature review to compile a list of 149 genes associated with ARKDs, including 31 glomerulopathies, 16 tubulopathies, 87 ciliopathies, and 15 congenital anomalies of the kidney and urinary tract (CAKUT). Disease-causing variants were collected from ClinVar, HGMD, LOVD, and our in-house database and evaluated for inclusion. Minor allele frequencies of 12,912 variants were then obtained from the Genome Aggregation Database (gnomAD) and the in-house database to estimate the lifetime risk. Results: The combined estimated lifetime risk was 27.49 per 100,000 (19.35–39.65) based on the European gnomAD dataset. The 3 disorders with the highest lifetime risk (>1.5 per 100,000), accounted for 24% of the overall lifetime risk and were caused by PKHD1 (autosomal recessive polycystic kidney disease), SLC12A3 (Gitelman syndrome), and COL4A3 (Alport syndrome) variants. Extrapolating to all modes of inheritance, the overall lifetime risk for monogenic kidney disease ranged from 1 in 611 to 1 in 498. Conclusion: This study offers a comprehensive population-genetic assessment of the lifetime risk associated with ARKDs focusing on European populations, shedding light on previously underestimated prevalences and diagnostic probabilities. Consequently, these findings provide crucial insights for optimizing resource allocation towards therapy development, enhancing public health strategies, and guiding future biomedical research endeavors.

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