Provides a function to calibrate variant effect scores against evidence strength categories defined by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines. The method computes likelihood ratios of pathogenicity via kernel density estimation of pathogenic and benign score distributions, and derives score intervals corresponding to ACMG/AMP evidence levels. This enables researchers and clinical geneticists to interpret functional and computational variant scores in a reproducible and standardised manner. For details, see Badonyi and Marsh (2025) &lt;<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtaf503" target="_top">doi:10.1093/bioinformatics/btaf503</a>&gt;.

A new score classifying dominant disease-causing mutations has found that just over half act via loss of function, with gain-of-function and dominant-negative mechanisms making up the rest.

Computational variant effect predictors (VEPs) and multiplexed assays of variant effect (MAVEs) are two key tools for assessing the functional consequences of genetic variants. While their outputs are...

Molecular disease mechanisms caused by mutations in protein-coding regions are diverse, but they can be broadly categorised into loss-of-function (LOF), gain-of-function (GOF), and dominant-negative (...