P-KNN: joint calibration of multiple pathogenicity prediction tools streamlines variant classification

Abstract

Purpose: Clinical guidelines for interpreting genetic variants in the context of Mendelian disease require converting the outputs of pathogenicity prediction tools into well-calibrated probabilities. However, the existing calibration method is only valid when pre-committing to one tool, preventing clinical laboratories from using multiple tools with complementary strengths. To lift this restriction, we introduce Pathogenicity K-Nearest Neighbors (P-KNN), a flexible method that jointly calibrates any set of tools. Methods: P-KNN represents each variant in a multidimensional space defined by tool scores and estimates the probability of pathogenicity based on the proportion of pathogenic neighbors. We compared P-KNN against standard single-tool calibration of multiple predictors and meta-predictors at four historical time points. Results: P-KNN outperforms standard calibration of single tools and meta-predictors in two aspects: i) overall evidence strength and ii) alignment of the calibrated probabilities with true pathogenicity frequencies. Additionally, the evidence from P-KNN keeps improving with the addition of newer tools. It also correctly integrates correlated computational and experimental evidence that is overestimated by existing protocols. Conclusion: P-KNN provides robust joint calibration for any set of pathogenicity prediction tools, thereby alleviating the constraint of pre-committing to a single predictor while enhancing statistical rigor and diagnostic yield. P-KNN is available via command line (https://github.com/Brandes-Lab/P-KNN) and precomputed scores (https://huggingface.co/datasets/brandeslab/P-KNN).