Type IV Collagen Variants in CKD: Performance of Computational Predictions for Identifying Pathogenic Variants
Cole Shulman, Emerald Liang, Misato Kamura, Khalil Udwan, Tony Yao, Daniel C. Cattran, Heather N. Reich, Michelle Hladunewich, York Pei, Judy Savige, Andrew D. Paterson, Mary Ann Suico, Hirofumi Kai, Moumita Barua
Abstract
RATIONALE & OBJECTIVE: variants. STUDY DESIGN SETTING & PARTICIPANTS: 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: 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: 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.