#1012 Lifetime risk of autosomal recessive kidney diseases calculated from genetic databases

遗传学 生物 医学 数据库 计算生物学 计算机科学
作者
Matthias C. Braunisch,Clara Großewinkelmann,Martin Menke,Nora Hannane,Jasmina Ćomić,Roman Guenthner,Lutz Renders,Christoph Schmaderer,Uwe Heemann,Korbinian Riedhammer,Matias Wagner,Julia Höfele
出处
期刊:Nephrology Dialysis Transplantation [Oxford University Press]
卷期号:39 (Supplement_1)
标识
DOI:10.1093/ndt/gfae069.249
摘要

Abstract Background and Aims Genetic kidney diseases represent an often rare and clinically diverse group. Their distinct clinical and genetic variations and biases in patient referrals and identification pose challenges in phenotype-based estimated prevalences. This study aimed to determine the calculated lifetime risk of autosomal recessive kidney diseases. Method We conducted a thorough literature review to compile a list of 149 genes linked to these diseases. Disease-causing variants were collected from ClinVar, HGMD, LOVD, and our database and reevaluated according to current guidelines. Minor allele frequencies were obtained from gnomAD and our database. We estimated the lifetime risk of autosomal recessive kidney diseases in a dataset of 12,800 disease-causing variants across 149 disease-associated genes (31 glomerulopathy, 16 tubulopathy, 87 ciliopathy, and 15 CAKUT genes). Results The combined estimated lifetime risk is 10.68 per 100,000 (95% confidence interval 6.29-18.40) based on our data and 22.77 per 100,000 (15.86-33.77) using the European gnomAD dataset. Notably, the three disorders with the highest lifetime risk (≥1.2 per 100,000), collectively accounting for 28% of the overall lifetime risk, were caused by PKHD1 (autosomal recessive polycystic kidney disease), SLC12A3 (Gitelman syndrome), and COL4A3 (Alport syndrome) variants (Fig. 1). Extrapolation to all modes of inheritance, the overall lifetime risk for monogenic kidney disease in adults ranged from 1 in 602 to 1 in 1,283 (Fig. 2). Conclusion This study provides a population-genetic estimate of lifetime risk for autosomal recessive kidney diseases across different populations, addressing underestimated prevalences and diagnostic probabilities. The data, therefore, informs resource allocation for therapy development, public health management, and biomedical research.
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