QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning

Abstract

Seqüenciació de nova generació; Diagnòstic genètic; Medicina de precisió

Next-generation sequencing; Genetic diagnostic; Precision medicine

Secuenciación de nueva generación; Diagnóstico genético; Medicina de precisión

Next-generation sequencing (NGS) has revolutionized genetic diagnostics, yet its application in precision medicine remains incomplete, despite significant advances in computational tools for variant annotation. Many variants remain unannotated, and existing tools often fail to accurately predict the range of impacts that variants have on protein function. This limitation restricts their utility in relevant applications such as predicting disease severity and onset age. In response to these challenges, a new generation of computational models is emerging, aimed at producing quantitative predictions of genetic variant impacts. However, the field is still in its early stages, and several issues need to be addressed, including improved performance and better interpretability. This study introduces QAFI, a novel methodology that integrates protein-specific regression models within an ensemble learning framework, utilizing conservation-based and structure-related features derived from AlphaFold models. Our findings indicate that QAFI significantly enhances the accuracy of quantitative predictions across various proteins. The approach has been rigorously validated through its application in the CAGI6 contest, focusing on ARSA protein variants, and further tested on a comprehensive set of clinically labeled variants, demonstrating its generalizability and robust predictive power. The straightforward nature of our models may also contribute to better interpretability of the results.

This work was supported by the Ministerio de Ciencia e Innovación de España through grants PID2019-111217RB-I00 and PID2022-142753OB-I00, both co-funded by the European Regional Development Fund (FEDER); and by grant TED2021-130342B-I00, funded by the Next Generation EU funds.