Quantifying prior probabilities for disease-causing variants reveals the top genetic contributors in inborn errors of immunity
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Background
Accurate interpretation of genetic variants requires a quantitative estimate of how likely a variant is to contribute to disease, accounting for both observed and unobserved causal alleles across different inheritance modes.
Methods
We developed a statistical framework that computes genome-wide prior probabilities for variant classification by integrating population allele frequencies, disease classifications, and Hardy-Weinberg expectations across dominant, recessive, and X-linked inheritance. Bayesian modelling then combines these priors with individual-level data to produce credible intervals that quantify diagnostic confidence.
Results
The framework replaces categorical variant classification with continuous posterior probabilities that capture residual uncertainty from incomplete or missing genotype data. Demonstrations in three diagnostic scenarios show accurate quantification of variant-level disease relevance. Application to 557 genes implicated in inborn errors of immunity (IEI) generated a public database of prior probabilities. Integration with protein-protein interaction and immunophenotypic data revealed gene-level constraint patterns, and validation in national cohorts showed close agreement between predicted and observed case numbers.
Conclusions
Our method addresses a long-standing gap in clinical genomics by quantifying both observed and unobserved genetic evidence in disease diagnosis. It provides a reproducible probabilistic foundation for variant interpretation, clinical decision-making, and large-scale genomic analysis. 1
Availability
This data is integrated in public panels at https://iei-genetics.github.io . The source code are accessible as part of the variant risk estimation project at https://github.com/DylanLawless/var_risk_est and IEI-genetics project at https://iei-genetics.github.io . The data is available from the Zenodo repository: https://doi.org/10.5281/zenodo.15111583 (Var-RiskEst PanelAppRex ID 398 gene variants.tsv). VarRiskEst is available under the MIT licence.
