Combining MAVEs and computational predictors improves variant classification across ancestries in hereditary cancer genes

2025-12-09 genetic and genomic medicine Title + abstract only

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Many commonly used computational tools for variant effect prediction exhibit ancestry-related bias because they are trained on clinical or population datasets that under-represent global diversity, leading to uneven and sometimes unfair variant classification across ancestries. Multiplexed assays of variant effect (MAVEs) and population-free VEPs instead offer alternatives that are unbiased with respect to human ancestry, providing classification evidence that generalises across populations. Her...

Combining MAVEs and computational predictors improves variant classification across ancestries in hereditary cancer genes

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