Choosing Variant Interpretation Tools for Clinical Applications: Context Matters

Abstract

Clinical variant interpretation; Healthcare costs; Pathogenicity prediction

Interpretación de variantes clínicas; Costes sanitarios; Predicción de patogenicidad

Interpretació de variants clíniques; Despeses sanitàries; Predicció de patogenicitat

Pathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers.

This work was supported by research grants SAF2016-80255-R from the Spanish Ministerio de Economía y Competitividad (MINECO), PID2019-111217RB-I00 and TED2021-130342B-I00 from the Spanish Ministerio de Ciencia e Innovación, and by the European Regional Development Fund (ERDF) through the Interreg program POCTEFA (Pirepred, EFA086/15).