The CADD score works by training a machine-learning model to distinguish variants that have survived natural selection from those that have been purged from the human population over evolutionary time. Variants that look like the ones evolution tends to eliminate receive high scores, expressed on a PHRED-like scale where a score of 20 means the variant is predicted to be more deleterious than the top one percent of all possible mutations. In practice, laboratories often use a fixed cutoff, frequently around 20 or higher, to filter the flood of variants that emerge from whole-exome or whole-genome sequencing of a patient. Anything below the threshold is deprioritized; anything above it is flagged for closer scrutiny by a diagnostic team. The elegance of this approach is its simplicity. The problem, as the new paper makes clear, is that the simplicity is deceptive.

The distribution of CADD scores is not uniform across the genome. Different genes and different gene sets carry different baseline distributions of scores, meaning that a single global cutoff can behave very differently depending on the context. A threshold that works well for one disease panel may be too permissive for another, quietly letting potentially pathogenic variants slip through, or too strict, burying diagnosticians under false positives. Compounding the issue, scores can shift between genome builds, the two most common being GRCh37 and GRCh38, and between CADD releases, such as versions 1.6 and 1.7. A variant scored under one combination of build and release may land on a slightly different point of the scale when re-scored under another. For a field where a single misclassified variant can change a patient’s diagnosis, these are not trivial discrepancies.

CADD-ThresholdApp tackles the calibration problem by grounding threshold selection in real clinical evidence. The application draws on ClinVar, the public archive that collects and classifies variants according to their reported clinical significance, ranging from benign to pathogenic. By visualizing the CADD-score distributions of clinically classified variants, the app lets users see, for any gene or set of genes, how benign and pathogenic variants actually separate along the score axis. Rather than guessing where to place the cutoff, users can watch how the overlap between the two distributions changes as the threshold moves, and choose a boundary that reflects the genuine diagnostic trade-off between sensitivity and specificity in their specific context.

Beyond the visualizations, the app reports threshold-dependent performance metrics, quantifying how well a chosen cutoff performs at discriminating pathogenic from benign variants within the selected gene set. This turns what has often been an informal, intuition-driven decision into a measurable one. Users can define their own genes or gene lists, which is essential for laboratories working on rare diseases where only a handful of candidate genes matter. The app also integrates curated gene panels from PanelApp, a widely used resource of expert-reviewed gene panels for clinical genomics, allowing diagnosticians to calibrate thresholds directly against the panels they actually deploy in practice.

One of the most technically significant features of the application is its support for comparisons across genome releases and CADD versions. Because GRCh37 and GRCh38 differ in their coordinate systems and in some sequence content, and because successive CADD releases incorporate updated annotations and retrained models, the same variant can carry different scores in different pipelines. The app allows users to examine how these differences affect threshold behavior, which is particularly valuable for laboratories transitioning between genome builds or updating their scoring pipelines while maintaining consistency in variant interpretation. Being able to compare v1.6 and v1.7 side by side within the same interface means that a lab can assess the practical impact of an upgrade before committing to it.

The developers also emphasized extensibility. CADD-ThresholdApp is implemented in a modular framework, meaning that it can be adapted to incorporate additional variant resources beyond ClinVar and additional deleteriousness scores beyond CADD. This design choice matters because the variant-interpretation landscape is crowded with competing scores, including SpliceAI, REVEL, AlphaMissense, and many others, and laboratories increasingly combine multiple metrics. A tool that can grow to accommodate new evidence sources is far more durable than one hard-wired to a single score. The modular architecture also invites community contributions, allowing other groups to extend the app to their own scoring systems without rebuilding it from scratch.

Accessibility was clearly a priority. The application is available as a hosted web service at cadd-threshold.kircherlab.bihealth.org, requiring nothing more than a browser, which lowers the barrier for smaller diagnostic laboratories and research groups without dedicated bioinformatics support. For institutions with stricter data policies or custom requirements, the app can be installed locally via container images, from the Python Package Index through PyPI, through the Bioconda package manager, or directly from source code. This range of deployment options reflects a pragmatic understanding of how computational tools actually get adopted in clinical settings, where reproducibility and local control are often non-negotiable requirements.

The significance of this work extends beyond a single tool. Variant interpretation sits at the heart of modern genomic medicine, and the choice of scoring thresholds directly influences which patients receive diagnoses and which variants are dismissed as benign. As whole-genome sequencing becomes routine in healthcare systems worldwide, the volume of variants requiring classification is growing faster than the expert capacity to review them, making automated prioritization scores like CADD indispensable. But an indispensable tool calibrated carelessly is a liability. By providing a transparent, evidence-based framework for context-specific threshold calibration, CADD-ThresholdApp addresses a genuine blind spot in the diagnostic pipeline, one that has rarely been discussed openly because the field has defaulted to convenient universal cutoffs.

The study, published as open-access software research with no competing interests declared, represents a collaboration rooted in the lab that originally developed the CADD score itself, with Martin Kircher among the co-authors and acknowledgments extending to the broader CADD team and supporting bioinformatics units at the Berlin Institute of Health at Charité and the University of Washington. For a discipline built on the promise of precision, there is something fitting about a tool whose entire purpose is precision in calibration, ensuring that the numbers geneticists rely on mean the same thing, whether they are examining a cardiac gene panel in Berlin or a rare-disease exome in Tokyo. As genomic medicine scales up, tools like this one may prove to be the quiet infrastructure that keeps the entire diagnostic enterprise honest.

Subject of Research: Calibration of CADD variant deleteriousness score thresholds using ClinVar for clinical variant interpretation

Article Title: CADD-ThresholdApp: an interactive web application for calibrating CADD scores using ClinVar

Article References: Leifheit, C., Kircher, M., & Schubach, M. (2026). CADD-ThresholdApp: an interactive web application for calibrating CADD scores using ClinVar. BMC Bioinformatics, 27(1), Article 224. https://doi.org/10.1186/s12859-026-06675-x

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06675-x

Keywords: CADD score, ClinVar, variant interpretation, genomics, bioinformatics, gene panels, threshold calibration, rare disease, web application, GRCh38, variant prioritization, machine learning