CausalCellInfer: Prioritizing Cell‐Type‐Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts

Integrating single‐cell RNA sequencing(scRNA‐seq) with the phenotypic depth of population‐scale biobanks is essential for elucidating the cellular basis of complex diseases. However, this is hindered by limited scRNA‐seq sample size and the lack of cellular resolution in biobanks. We present CausalCellInfer, a scalable computational framework bringing cellular resolution to bulk and genotype‐imputed transcriptomes. CausalCellInfer employs an invariant causal prediction‐inspired method(scRNA‐seq‐based invariant generalized covariance measure, scI‐GCM) to select environment‐stable marker genes, a parsimonious deep neural network for cell‐fraction deconvolution, and regularized matrix completion to reconstruct cell‐type‐specific (CTS) expression profiles. This design is optimized for biobank‐scale data with technical heterogeneity and limited gene overlap. Validated across simulated data, pseudo‐bulk and real peripheral blood mononuclear cell (PBMC) datasets, CausalCellInfer demonstrated superior accuracy and efficiency over existing methods. Applied to ∼500 000 UK Biobank (UKBB) participants across 29 traits, it captured known pathological shifts, such as reduced pancreatic β‐cell proportions in type 2 diabetes, and identified novel biological signals, including disrupted excitatory neuron‐oligodendrocyte interactions in depression. Inferred CTS differential expression patterns showed significant concordance with independent single‐cell studies and were enriched for Open Targets disease genes. Overall, CausalCellInfer bridges single‐cell insights and population‐scale genomics, enabling systematic discovery of putative disease mechanisms at cellular resolution.