| Title: | CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts |
| Journal: | Advanced Science |
| Published: | 14 Oct 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42875726/ |
| DOI: | https://doi.org/10.1002/advs.78197 |
Publication 21521
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Abstract
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.</p>
7 Authors
- Liangying Yin
- Yujia Shi
- Ruoyu Zhang
- Yong Xiang
- Jinghong Qiu
- Pak-Chung Sham
- Hon-Cheong So
Enabling scientific discoveries that improve human health