Xiao‐Ou Shu

The paper performs single-cell transcriptome-wide association analysis to identify cell-type-specific genetic susceptibility genes for colorectal cancer, finding 546 gene-cell-type associations and 347 candidate susceptibility genes.

The paper uses deconvolution to identify cell-type-specific DNA methylation and gene expression associations with colorectal cancer risk, finding 178 CpG sites and 68 genes across 26 new loci.

This study identifies novel genetic risk loci for pancreatic ductal adenocarcinoma in African ancestry individuals through admixture mapping and GWAS.

This study integrates large-scale omics and electronic health records to identify risk proteins and drugs for cancer prevention, finding 101 novel proteins and three drugs associated with reduced colorectal cancer risk.

This study identifies 60 new susceptibility loci and 441 candidate risk genes for inflammatory bowel disease through multi-ancestry GWAS and TWAS, with implications for precision medicine.

The paper develops and validates multi-ancestry polygenic risk scores for LUAD in East Asian never-smokers, showing improved risk prediction compared to East Asian-only models.

This study introduces transTF-TWAS, a method that integrates transcription factor-linked trans-located variants into transcriptome-wide association analyses to improve disease risk gene discovery.

The paper identifies 58 risk genes associated with common cancers by linking genetically predicted APA levels to cancer risk using GWAS data.

The paper applies transfer learning to adapt a general-purpose model to a specific domain using limited data, demonstrating improved performance in breast cancer-related tasks.

This study investigates the clinical consequences of a genetic predisposition to lower white blood cell counts using a polygenic score and finds associations with bone marrow biopsy outcomes and treatment responses.

The paper identifies tissue-specific DNA methylation biomarkers for cancer risk by integrating genome and epigenome data from the GTEx consortium and GWAS data.

The paper identifies novel genetic loci associated with blood pressure that interact with sleep duration across multiple ancestries.

The paper uses a multi-layered approach combining GWAS, spatial autocorrelation, and Hi-C to identify novel genetic variants associated with pancreatic cancer susceptibility.

This paper identifies 56 new type 2 diabetes loci in 433,540 East Asian individuals through a meta-analysis of GWAS data.

The paper identifies 49 novel blood lipid loci associated with gene-sleep interactions in 126,926 individuals across 5 ancestry groups, with gene-short-sleep interactions explaining 4.25% of triglyceride variance.