The paper introduces cellSTAAR, a novel statistical framework designed to improve the discovery of rare genetic variants within noncoding regions of the human genome. By integrating single-cell epigenetic data with whole-genome sequencing, the method identifies functional associations that are often masked in traditional bulk tissue studies. The tool specifically addresses the difficulty of linking candidate cis-regulatory elements to their target genes by utilizing an ensemble of diverse computational approaches. Applications of the model to large-scale datasets, such as the UK Biobank and TOPMed, demonstrate its superior power in detecting meaningful biological signals for complex traits like lipid levels. Ultimately, cellSTAAR enhances the interpretability of genetic studies by prioritizing the most relevant cell types for specific human diseases.

References:

• Van Buren E, Zhang Y, Li X, et al. cellSTAAR: incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of noncoding regions[J]. Nature Methods, 2025: 1-12.