Single-cell data combined with phenotypes improves variant interpretation

Whole genome sequencing offers significant potential to improve the diagnosis and treatment of rare diseases by enabling the identification of thousands of rare, potentially pathogenic variants. Existing variant prioritisation tools can be complemented by approaches that incorporate phenotype specificity and provide contextual biological information, such as tissue or cell-type specificity.

Citation:
Chapman T, Lassmann T. Single-cell data combined with phenotypes improves variant interpretation. BMC Genomics. 2025;26(1).

Keywords:
Interpretable models; Machine learning; Random forest; Rare disease; Variant prioritisation; Whole Genome sequencing

Abstract:
Whole genome sequencing offers significant potential to improve the diagnosis and treatment of rare diseases by enabling the identification of thousands of rare, potentially pathogenic variants. Existing variant prioritisation tools can be complemented by approaches that incorporate phenotype specificity and provide contextual biological information, such as tissue or cell-type specificity.