A New Era in Missense Variant Analysis: Statistical Insights and the Introduction of VAMPP-Score for Pathogenicity Assessment

Abstract

The clinical interpretation of missense variants is critically important in diagnostics due to their potential to cause mild-to-severe effects on phenotype by altering protein structure. Evaluating these variants is essential because they can significantly impact disease outcomes and patient management. Many computational predictors, known as in silico pathogenicity predictors (ISPPs), have been developed to support the assessment of variant pathogenicity. Despite the abundance of these ISPPs, their predictions often lack accuracy and consistency, primarily due to limited data availability and the presence of erroneous data. This inconsistency can lead to false positive or negative results in pathogenicity evaluation, highlighting the need for standardization. The necessity for reliable evaluation methods has driven the development of numerous ISPPs, each attempting to address different aspects of variant interpretation. However, the sheer number of ISPPs and their varied performances make it challenging to achieve consensus in predictions. Therefore, a comprehensive statistical approach to evaluate and integrate these predictors is essential to improve accuracy. Here, we present a comprehensive statistical analysis comparing 52 available ISPPs, which aims to enhance the precision of variant classification. Our work introduces the Variant Analysis with Multiple Pathogenicity Predictors-score (VAMPP-score), a novel statistical framework designed for the assessment of missense variants. The VAMPP-score leverages the best gene-ISPP matches based on ISPP accuracies, providing a combinatorial weighted score that improves missense variant interpretation. We chose to develop a statistical framework rather than creating a new ISPP to capitalize on the strengths of existing predictors and to address their limitations through an integrative approach. This approach not only improves the evaluation of missense variants but also offers a flexible statistical framework designed to identify and utilize the best-performing ISPPs. By enhancing the accuracy of genetic diagnostics, particularly in the reanalysis of rare and undiagnosed cases, our framework aims to improve patient outcomes and advance the field of genetic research. Our study employed a comprehensive workflow (Figure 1) to enhance the accuracy of genomic variant interpretation with in-silico pathogenicity predictor (ISPP) evaluation. This workflow led to three pivotal results: O_FIG O_LINKSMALLFIG WIDTH=171 HEIGHT=200 SRC="FIGDIR/small/602867v1_fig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@fc41f4org.highwire.dtl.DTLVardef@14e1d8eorg.highwire.dtl.DTLVardef@1765c75org.highwire.dtl.DTLVardef@1b00719_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Methodological summary. A database was built using dbNSFP (1, 2) (v4.7A) data, which includes rank scores from over fifty computational predictors for all possible single nucleotide variations in the human genome. This database was enhanced with canonical transcript information from Ensembl. The analysis focused on missense variants in ClinVar (3), categorized as Pathogenic (P), Benign (B), and Unknown (U). A novel statistical framework was used to assess the gene-based performance of each ISPP in distinguishing these variant groups through multiple and pairwise comparisons and proximity estimations. ISPPs were categorized (Category A-D) and assigned weights based on their performance for each gene, known as gene coefficients. These were integrated into the final assessment, the VAMPP-score. C_FIG[bullet] ISPPs were categorized on their prediction approaches. This classification not only streamlined the analytical process but also enhanced the interpretability of predictor outputs. [bullet]Leveraging this categorization, we conducted a robust statistical analysis to evaluate the prediction accuracy and performance of each ISPP. Our findings revealed a significant correlation between the prediction approaches of the ISPPs and their predictive successes, confirming the utility of our categorization approach. [bullet]These insights enabled us to develop a novel scoring system--the VAMPP-score--which integrates ISPPs according to their performances.