Variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2

Abstract

CYP21A2 deficiency represents 95% of congenital adrenal hyperplasia (CAH) cases, a group of genetic disorders that affect steroid biosynthesis. The genetic and functional analysis provide critical tools to elucidate complex CAH cases. One of the most accessible tools to infer the pathogenicity of new variants is in silico prediction. Here, we analyzed the performance of in silico prediction tools to categorize missense single nucleotide variants (SNVs) of CYP21A2. SNVs of CYP21A2 characterized in vitro by functional assays were selected to assess the performance of online single and meta predictors. SNVs were tested separately or in combination with the related phenotype (severe or mild CAH form). In total, 103 SNVs of CYP21A2 (90 pathogenic and 13 neutral) were used to test the performance of 13 single-predictors and four meta-predictors. All SNVs associated with the severe phenotypes were well categorized by all tools, with an accuracy of between 0.69 (PredictSNP2) and 0.97 (CADD), and Matthews’ correlation coefficient (MCC) between 0.49 (PoredicSNP2) and 0.90 (CADD). However, SNVs related to the mild phenotype had more variation, with the accuracy between 0.47 (S3Ds&GO and MAPP) and 0.88 (CADD), and MCC between 0.18 (MAPP) and 0.71 (CADD). From our analysis, we identified four predictors of CYP21A2 variant pathogenicity with good performance, CADD, ConSurf, DANN, and PolyPhen2. These results can be used for future analysis to infer the impact of uncharacterized SNVs in CYP21A2.

Comprehensive and accurate genome analysis at scale using DRAGEN accelerated algorithms

“…The recombination of gene and pseudogene can lead to deletions of part of each copy, generating gene-pseudogene fusions. The variants across CYP21A2 can lead to Congenital Adrenal Hyperplasia 31 . GBA is an important target gene due to variants that increase risk for Parkinson's and Gaucher's disease and Lewy body dementia 32,33 .…”

Exploring Novel Variants of the Cytochrome P450 Reductase Gene (POR) from the Genome Aggregation Database by Integrating Bioinformatic Tools and Functional Assays

“…The area under the curve (AUC) is a metric used to assess the performance of classification models. Tools with AUC values ranging from 0.85 to 1 exhibited excellent predictive accuracy, suggesting their effectiveness in accurately classifying variants (Figure 2) [57,58]. On the other hand, SIFT, MAPP, and PolyPhen-1 and -2, with AUC values between 0.45 and 0.6, showed poorer performance and were consequently excluded from the analysis of the missense variants from gnomAD (Figure 2).…”