J Natl Cancer Inst. 2026 Apr 9:djag108. doi: 10.1093/jnci/djag108. Online ahead of print.
ABSTRACT
BACKGROUND: Transcriptome-wide association studies (TWAS) integrate gene expression and genome-wide association studies (GWAS) to identify disease susceptibility genes. Because gene expression varies substantially across cell types within tissues, cell type-specific prediction models may enhance the power of TWAS.
METHODS: We conducted cell type-specific TWAS leveraging single-cell RNA sequencing data from the OneK1K cohort (14 immune cell types, 1.27 million cells) and GWAS summary statistics for seven cancers (>290,000 cases in total). To improve prediction accuracy, we developed a modeling framework that incorporates shared gene expression effects across cell types.
RESULTS: At a false discovery rate of 5% (Bonferroni 5%), we identified 106 (13) previously unreported loci for breast cancer, 51 (4) loci for prostate cancer, 11 (4) loci for lung cancer, 39 (5) loci for melanoma, 9 (1) loci for ovarian cancer, and 2 (1) loci for diffuse large B-cell lymphoma, with most genes exhibiting cell type specificity. Gene set analyses confirmed joint associations of unreported genes with breast and prostate cancer risk in UK Biobank data. Additional lung tissue scRNA-seq data with 113 individuals validated 18 of 32 (56.3%) significant genes for lung cancer. Across cancers, 139 significant genes were shared by at least two cancer types and were primarily enriched in specific immune cell types.
CONCLUSION: Cell type-specific TWAS improves the identification of novel cancer susceptibility loci and provides insights into the immune landscape of cancer etiology.
PMID:41967136 | DOI:10.1093/jnci/djag108
