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

Overview of attention for article published in Pediatric Nephrology, May 2013
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Title
DNA variant databases improve test accuracy and phenotype prediction in Alport syndrome
Published in
Pediatric Nephrology, May 2013
DOI 10.1007/s00467-013-2486-8
Pubmed ID
Authors

The International Alport Mutation Consortium, 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

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than 700 variants have been described and a further 400 are estimated to be known to individual laboratories but are unpublished.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testing laboratories for X-linked Alport syndrome worldwide have established a Web-based database for published and unpublished COL4A5 variants ( https://grenada.lumc.nl/LOVD2/COL4A/home.php?select_db=COL4A5 ).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.

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Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Student > Master 6 18%
Other 3 9%
Student > Doctoral Student 3 9%
Student > Bachelor 3 9%
Other 9 27%
Unknown 2 6%
Readers by discipline Count As %
Medicine and Dentistry 13 39%
Biochemistry, Genetics and Molecular Biology 6 18%
Agricultural and Biological Sciences 4 12%
Psychology 2 6%
Nursing and Health Professions 1 3%
Other 5 15%
Unknown 2 6%