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CIGMA tool maps how gene expression variants differ across individual cell types

Researchers introduced CIGMA, a statistical method that uses a linear mixed model to separate genetic effects on gene expression into components shared across cell types and components specific to individual cell types, applied to single-cell RNA sequencing data. The model uses cell-type-level pseudobulk expression averaged from single-cell data, requiring more than 10 cells per individual per cell type, and explicitly accounts for cell-to-cell noise within each individual and cell type.