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Background <p>Autosomal-dominant tubulointerstitial kidney disease caused by <i>MUC1</i> (ADTKD-<i>MUC1</i>) is a rare disorder characterized by progressive kidney dysfunction. Pathogenic variants in <i>MUC1</i> are difficult to detect owing to the variable number tandem repeat region. To address this issue, VNtyper-Kestrel, a bioinformatics pipeline for short-read sequencing data, was recently developed. In this study, the performance of VNtyper-Kestrel for detecting <i>MUC1</i> variants in clinical settings was evaluated.</p> Methods <p>We used VNtyper-Kestrel to retrospectively analyze short-read sequencing data for 209 individuals with suspected ADTKD who were previously evaluated through long-read sequencing. Data from a panel including ~ 180 genes and an ADTKD-specific panel were used. In addition, the pipeline was applied to 976 patients with suspected hereditary kidney diseases other than ADTKD and positive cases were validated using long-read sequencing. Accuracy was assessed by comparisons with the results of long-read sequencing.</p> Results <p>Using VNtyper-Kestrel, we identified <i>MUC1</i> variants in 16 of 19 confirmed cases of ADTKD-<i>MUC1</i>. Three initially negative cases were reanalyzed using the ADTKD-specific panel, yielding positive detection results with high confidence. We obtained two low-confidence positive results from 190 cases of suspected ADTKD and 10 low-confidence positive results among 976 non-ADTKD cases; however, all were classified as false positives upon long-read sequencing validation.</p> Conclusions <p>VNtyper-Kestrel demonstrated high sensitivity in identifying <i>MUC1</i> variants when sequencing coverage was adequate, supporting its potential as a rapid and cost-effective screening tool. However, confirmatory long-read sequencing is needed in uncertain cases. Optimizing coverage and refining patient selection criteria could improve the clinical utility of this approach.</p>

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Clinical use of the VNtyper-Kestrel pipeline for MUC1 variant detection in autosomal-dominant tubulointerstitial kidney disease

  • 期刊论文

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  • 发表日期：

    2025年4月17日

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  • China Nagano,
  • Naoya Morisada,
  • Yuta Inoki,
  • Yu Tanaka,
  • Yuta Ichikawa,
  • Chika Ueda,
  • Hideaki Kitakado,
  • Yuya Aoto,
  • Nana Sakakibara,
  • Tomoko Horinouchi,
  • Tomohiko Yamamura,
  • Shingo Ishimori,
  • Kandai Nozu

摘要

Background

Autosomal-dominant tubulointerstitial kidney disease caused by MUC1 (ADTKD-MUC1) is a rare disorder characterized by progressive kidney dysfunction. Pathogenic variants in MUC1 are difficult to detect owing to the variable number tandem repeat region. To address this issue, VNtyper-Kestrel, a bioinformatics pipeline for short-read sequencing data, was recently developed. In this study, the performance of VNtyper-Kestrel for detecting MUC1 variants in clinical settings was evaluated.

Methods

We used VNtyper-Kestrel to retrospectively analyze short-read sequencing data for 209 individuals with suspected ADTKD who were previously evaluated through long-read sequencing. Data from a panel including ~ 180 genes and an ADTKD-specific panel were used. In addition, the pipeline was applied to 976 patients with suspected hereditary kidney diseases other than ADTKD and positive cases were validated using long-read sequencing. Accuracy was assessed by comparisons with the results of long-read sequencing.

Results

Using VNtyper-Kestrel, we identified MUC1 variants in 16 of 19 confirmed cases of ADTKD-MUC1. Three initially negative cases were reanalyzed using the ADTKD-specific panel, yielding positive detection results with high confidence. We obtained two low-confidence positive results from 190 cases of suspected ADTKD and 10 low-confidence positive results among 976 non-ADTKD cases; however, all were classified as false positives upon long-read sequencing validation.

Conclusions

VNtyper-Kestrel demonstrated high sensitivity in identifying MUC1 variants when sequencing coverage was adequate, supporting its potential as a rapid and cost-effective screening tool. However, confirmatory long-read sequencing is needed in uncertain cases. Optimizing coverage and refining patient selection criteria could improve the clinical utility of this approach.