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DNA variant databases improve test accuracy and phenotype prediction in Alport syndrome

  • Judy Savige
  • , Elisabet Ars
  • , Richard G.H. Cotton
  • , David Crockett
  • , Hayat Dagher
  • , Constantinos Deltas
  • , Jie Ding
  • , Frances Flinter
  • , Genevieve Pont-Kingdon
  • , Nizar Smaoui
  • , Roser Torra
  • , Helen Storey

Research output: Contribution to journalReview articlepeer-review

Abstract

X-linked Alport syndrome is a form of progressive renal failure caused by pathogenic variants in the COL4A5 gene. More than 700 variants have been described and a further 400 are estimated to be known to individual laboratories but are unpublished. The major genetic testing laboratories for X-linked Alport syndrome worldwide have established a Web-based database for published and unpublished COL4A5 variants ( https://grenada.lumc.nl/LOVD2/COL4A/home.php? select-db=COL4A5 ). This conforms with the recommendations of the Human Variome Project: it uses the Leiden Open Variation Database (LOVD) format, describes variants according to the human reference sequence with standardized nomenclature, indicates likely pathogenicity and associated clinical features, and credits the submitting laboratory. The database includes non-pathogenic and recurrent variants, and is linked to another COL4A5 mutation database and relevant bioinformatics sites. Access is free. Increasing the number of COL4A5 variants in the public domain helps patients, diagnostic laboratories, clinicians, and researchers. The database improves the accuracy and efficiency of genetic testing because its variants are already categorized for pathogenicity. The description of further COL4A5 variants and clinical associations will improve our ability to predict phenotype and our understanding of collagen IV biochemistry. The database for X-linked Alport syndrome represents a model for databases in other inherited renal diseases.

Original languageEnglish
Pages (from-to)971-977
Number of pages7
JournalPediatric Nephrology
Volume29
Issue number6
DOIs
Publication statusPublished - Jun 2014
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Alport syndrome
  • DNA database
  • Gene variant
  • Genetic testing
  • Inherited renal disease

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