ArticleKidney medicine
Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants.
Article in Kidney medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Constructing networks for comparison of collagen types.Journal of integrative bioinformatics · 2024Article
- A case report: Alport syndrome and growth hormone deficiency associated with a newTranslational pediatrics · 2024Article
- The Phenotypic Spectrum ofKidney international reports · 2023Article
- Alport Syndrome: Clinical Spectrum and Therapeutic Advances.Kidney medicine · 2023Review
- Interpreting ciliopathy-associated missense variants of uncertain significance (VUS) in Caenorhabditis elegans.Human molecular genetics · 2022Article
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Authors and funding
14 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RATIONALE &
objectivePathogenic variants in type IV collagen have been reported to account for a significant proportion of chronic kidney disease. Accordingly, genetic testing is increasingly used to diagnose kidney diseases, but testing also may reveal rare missense variants that are of uncertain clinical significance. To aid in interpretation, computational prediction (called in silico) programs may be used to predict whether a variant is clinically important. We evaluate the performance of in silico programs for STUDY DESIGN SETTING &
participantsRare missense variants in TESTS COMPARED & OUTCOMES: All rare missense variants identified in the 4 disease cohorts were subjected to in silico predictions using 12 different programs. Comparisons between the predictions were compared with: (1) variant classification (pathogenic or benign) in the cohorts and (2) functional characterization in a randomly selected smaller number (17) of pathogenic or uncertain significance variants obtained from the local FSGS cohort.
resultsIn silico predictions correctly classified 75% to 97% of pathogenic and 57% to 100% of benign LIMITATIONS: Overestimation of in silico program sensitivity given that they may have been used in the categorization of variants labeled as pathogenic in disease repositories.
conclusionsOur results suggest that in silico predictions are sensitive but not specific to assign
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