Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants
| Autor: | Judy Savige, Tony Yao, Hirofumi Kai, York Pei, Heather N. Reich, Michelle Hladunewich, Andrew D. Paterson, Daniel C. Cattran, Emerald Liang, Cole Shulman, Mary Ann Suico, Misato Kamura, Moumita Barua, Khalil Udwan |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
In silico Genomics Computational biology Disease Biology LOVD Internal Medicine medicine genomics Missense mutation Clinical significance Genetic testing Original Research medicine.diagnostic_test ARUP computational predictions ClinVar gnomAD FSGS Nephrology Cohort Medical genetics in silico predictions Alport syndrome type IV collagen variants |
| Zdroj: | Kidney Medicine |
| ISSN: | 2590-0595 |
| Popis: | Rationale & Objective Pathogenic 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 COL4A3/A4/A5 variants. Study Design, Setting, & Participants Rare missense variants in COL4A3/A4/A5 were identified in disease cohorts, including a local focal segmental glomerulosclerosis (FSGS) cohort and publicly available disease databases, in which they are categorized as pathogenic or benign based on clinical criteria. 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. Results In silico predictions correctly classified 75% to 97% of pathogenic and 57% to 100% of benign COL4A3/A4/A5 variants in public disease databases. The congruency of in silico predictions was similar for variants categorized as pathogenic and benign, with the exception of benign COL4A5 variants, in which disease effects were overestimated. By contrast, in silico predictions and functional characterization classified all 9 pathogenic COL4A3/A4/A5 variants correctly that were obtained from a local FSGS cohort. However, these programs also overestimated the effects of genomic variants of uncertain significance when compared with functional characterization. Each of the 12 in silico programs used yielded similar results. 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. Conclusions Our results suggest that in silico predictions are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants and variants of uncertain significance. Thus, we do not recommend in silico programs but instead recommend pursuing more objective levels of evidence suggested by medical genetics guidelines. |
| Databáze: | OpenAIRE |
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