BRCA1 and BRCA2 (BRCA1/2) germline variants disrupting the DNA protective role of these genes increase the risk of hereditary breast and ovarian cancers. Correct identification of these variants then becomes clinically relevant, because it may increase the survival rates of the carriers. Unfortunately, we are still unable to systematically predict the impact of BRCA1/2 variants. In this article, we present a family of in silico predictors that address this problem, using a gene‐specific approach. For each protein, we have developed two tools, aimed at predicting the impact of a variant at two different levels: Functional and clinical. Testing their performance in different datasets shows that specific information compensates the small number of predictive features and the reduced training sets employed to develop our models. When applied to the variants of the BRCA1/2 (ENIGMA) challenge in the fifth Critical Assessment of Genome Interpretation (CAGI 5) we find that these methods, particularly those predicting the functional impact of variants, have a good performance, identifying the large compositional bias towards neutral variants in the CAGI sample. This performance is further improved when incorporating to our prediction protocol estimates of the impact on splicing of the target variant.

Outside Front Cover: The cover image is based on the Special Article BRCA1‐ and BRCA2‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge by Natàlia Padilla et al., https://doi.org/10.1002/humu.23802. Cover design: Selen Özkan and Xavier de la Cruz.

Motivation Our inability to solve the Variant Interpretation Problem (VIP) has become a bottleneck in the biomedical/clinical application of Next-Generation Sequencing. This situation has favored the development and use of in silico tools for the VIP. However, choosing the optimal tool for our purposes is difficult because of a fact usually ignored: the high variability of clinical context/scenarios across and within countries, and over time. Results We have developed a computational procedure, based on the use of cost models, that allows the simultaneous comparison of an arbitrary number of tools across all possible clinical scenarios. We apply our approach to a set of pathogenicity predictors for missense variants, showing how differences in clinical context translate to differences in tool ranking. Availability The source code is available at: https://github.com/ClinicalTranslationalBioinformatics/clinical_space_partition

Predicting how missense variants alter protein function is central to resolving the genotype-to-phenotype equation, a task now addressed by numerous computational predictors. The most accurate are increasingly opaque, however, limiting both their mechanistic value and their adoption in the clinic. Here we present QAFIsplit, a redesign of the Quantitative Assessment of Functional Impact framework that models variant effect in two interpretable stages: the mutation tolerance of a position, and the deviation introduced by a specific substitution. Framed as the prediction of a molecular endophenotype, QAFIsplit reaches state-of-the-art quantitative accuracy on independent deep mutational scanning benchmarks, surpassing black-box predictors such as AlphaMissense in most protein domains. Carried into clinical variant classification, it performs on par with established clinical tools without ever being trained on clinical labels, and complements meta-predictors without penalty. In the CAGI 7 community experiment, QAFI-based strategies were submitted blindly to four independent disease-gene challenges, backed by strong pre-submission clinical validation (AUC up to 0.99).

Predicting how missense variants alter protein function is central to resolving the genotype-to-phenotype equation, a task now addressed by numerous computational predictors. The most accurate are increasingly opaque, however, limiting both their mechanistic value and their adoption in the clinic. Here we present QAFIsplit, a redesign of the Quantitative Assessment of Functional Impact framework that models variant effect in two interpretable stages: the mutation tolerance of a position, and the deviation introduced by a specific substitution. Framed as the prediction of a molecular endophenotype, QAFIsplit reaches state-of-the-art quantitative accuracy on independent deep mutational scanning benchmarks, surpassing black-box predictors such as AlphaMissense in most protein domains. Carried into clinical variant classification, it performs on par with established clinical tools without ever being trained on clinical labels, and complements meta-predictors without penalty. In the CAGI 7 community experiment, QAFI-based strategies were submitted blindly to four independent disease-gene challenges, backed by strong pre-submission clinical validation (AUC up to 0.99).