medRxivGenetic & Genomic MedicinePreprint
Single-cell transcriptome-wide association analysis reveals epithelial cell-specific genetic susceptibility to colorectal cancer
Qing Li, Chao Li, Linshuoshuo LyuiD, Jifeng Wang, Fangcheng YuaniD, Eugene JeongiD, Qingyuan Song, Chao YaniD, You CheniD, Jungyoon Choi, Shuai XuiD, Yu WangiD, Quanhu ShengiD, Wanqing WeniD, Martha J. ShrubsoleiD, Qiuyin CaiiD, Xiao‐Ou ShuiD, Guimin GaoiD, Bhuminder SinghiD, Zhijun YiniD, Lori A. CoburniD, Kristen Keon CiomboriD, Cathy EngiD, Joshua A. BaueriD, Ulrike PetersiD, , Xingyi GuoiD
Abstract
Bulk-tissue transcriptome-wide association studies (TWAS) have identified colorectal cancer (CRC) susceptibility genes, but cell-type-specific regulatory mechanisms remain poorly understood. Here, we performed single-cell TWAS integrating matched germline genotypes and single-cell transcriptomes from normal colorectal mucosa (381 donors) and polyps (84 donors), cell-type-specific chromatin accessibility, and GWAS from 132,261 cases and 1,752,398 controls. We identified 546 gene-cell-type associations implicating 347 candidate susceptibility genes across seven normal and nine polyp epithelial populations, including 213 previously unreported genes, with 74% exhibiting cell-type-specific associations. Integrated co-expression and ChIP-seq analyses revealed distinct TF-risk gene networks, highlighting GATA6, MAX and RAD21 in normal mucosa, and TFAP4- and CEBPB-associated networks in premalignant polyp populations. Further multi-omics analyses supported dysregulation of 233 genes (67%), while drug-target analyses identified 63 potentially druggable genes. DepMap and CRISPR-Cas9 analyses implicated 35 genes in CRC cell viability. These findings reveal cell-type-specific susceptibility mechanisms and prioritize candidate therapeutic targets for CRC prevention.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- assay: ChIP-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
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