The missing element in genomic interpretation.
Database, scan, and state-of-the-art algorithm.
For over a century, we have known how genetic variants should behave. But we have never had a system that measures, with statistical confidence, whether a given genome fits that expectation.
Traditional approaches stop at labels like pathogenic, benign, or damaging. These are useful shortcuts, but too limited for clinical, pharmacogenomic, or genome engineering.
Quant replaces labels with a probabilistic framework. It estimates how likely any variant, observed or not, explains a functional effect.
For every possible variant and effect combination, we get:
“Overall probability of a correct causal diagnosis due to SNV/INDEL with current evidence: 0.511 (95% CrI: 0.237–0.774).”
The same structure applies in numerous scenarios including rare disease diagnosis, drug target selection, variant reclassification, and genome design.
Built on Hardy-Weinberg theory, Bayesian inference, and population-scale priors, Quant turns variant interpretation into a reproducible, rigorous system.
Who it’s for
Quant is built for universal use by:
- Global biotech companies
- Major hospital systems
- Small, focused research teams
Across cutting-edge applications:
- AI models for genomic interpretation
- Genome editing and design platforms
- Large-scale population reference datasets
- Clinical diagnostics and variant pipelines
- Digital twin platforms for personalised simulation
A complete inference system
- Quant DB provides genome-wide calibrated priors for every variant.
- Quant Scan extracts observed and unobserved variant evidence from each genome.
- Quant Calc integrates the data and computes the probability that any variant explains the disease, with a credible interval.
The result is a rigorous, interpretable framework for genomic inference across diagnostics, discovery, and design.
Validated in national studies. AI-ready format. Built for clinical labs, research pipelines, and real-world use.
Publications and data
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Quant: The quantitative omic epidemiology group, et al. “Quantifying prior probabilities for disease-causing variants reveals the top genetic contributors in inborn errors of immunity” medRxiv preprint (2025).
DOI | PDF | Video -
Genetic diagnosis of inborn-errors of immunity (IEI): open-source, world-leading database of IEI genetics provided as a subset of PanelAppRex for the community.
Homepage | Database | Repository
Releases and technical access
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Quant calc 001: Base model with structured priors, full statistical framework, and real-world application. (Early access partners)
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Quant calc 000: Minimal prototype showcasing core Bayesian engine, public data, and open-source code.
DOI | PDF | Video -
Genetic diagnosis of inborn-errors of immunity (IEI): open-source, world-leading database of IEI genetics provided as a subset of PanelAppRex for the community.
Homepage | Database | Repository
For a full list of publications and software releases, visit the
publications page
release archive
Technically sound. Incredibly simple. The missing element in genomic interpretation.
