henhaobio
Sign in

medRxivGenetic & Genomic MedicinePreprint

SNPred outperforms other ensemble-based SNV pathogenicity predictors and elucidates the challenges of using ClinVar for evaluation of variant classification quality.

Иван Игоревич МолотковiD, Daniel C. KoboldtiD, Mykyta ArtomoviD

PDFDOI

Abstract

BackgroundCurrent single nucleotide variants (SNVs) pathogenicity prediction tools assess various properties of genetic variants and provide a likelihood of causing a disease. This information aids in variant prioritization - the process of narrowing down the list of potential pathogenic variants, and, therefore, facilitating diagnostics. Assessing the effectiveness of SNV pathogenicity tools using ClinVar data is a widely adopted practice. Our findings demonstrate that this conventional method tends to overstate performance estimates. MethodsWe introduce SNPred, an ensemble model specifically designed for predicting the pathogenicity of nonsynonymous single nucleotide variants (nsSNVs). To evaluate its performance, we conducted assessments using six distinct validation datasets derived from ClinVar and BRCA1 Saturation Genome Editing (SGE) data. ResultsAcross all validation scenarios, SNPred consistently outperformed other state-of-the-art tools, particularly in the case of rare and cancer-related variants, as well as variants that are classified with low confidence by most in silico tools. To ensure convenience, we provide precalculated scores for all possible nsSNVs. We proved that the exceptionally high accuracy scores of the best models achieved for ClinVar variants are only attainable if the models learn to replicate misclassifications found in ClinVar. Additionally, we conducted a comparison of predictor performance on two distinct sets of BRCA1 variants that did not overlap: one sourced from ClinVar and the other from the SGE study. Across all in silico predictors, we observed a significant trend where ClinVar variants were classified with notably higher accuracy. ConclusionsWe provide a powerful variant pathogenicity predictor that enhances the quality of clinical variant interpretation and highlights important challenges of using ClinVar for SNV pathogenicity predictors evaluation.

Citations
7

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISGunning '20Harrizi '26Mor '26Bhat '22Lin '24Cabrera-Ala '20
64% semantic matchGenomics
Adam C. Gunning, Verity Fryer, James Fasham+4 more·Journal of Medical Genetics·Aug 25, 2020· 134 citations