{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chang YH"],"funding":["U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)","U.S. Department of Health &amp; Human Services | NIH | National Cancer Institute","NCI NIH HHS"],"pagination":["874-885"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12449480"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["133(6)"],"pubmed_abstract":["<h4>Background</h4>Integrating genome-wide association study (GWAS) and transcriptomic datasets can identify mediators for genetic risk of cancer. Traditional methods often are insufficient as they rely on total gene expression measures and overlook alternative splicing, which generates different transcript-isoforms with potentially distinct effects.<h4>Methods</h4>We integrate multi-tissue isoform expression data from the Genotype Tissue-Expression Project with GWAS summary statistics (all N > ~20,000 cases) to identify isoform- and gene-level associations with six cancers (breast, endometrial, colorectal, lung, ovarian, prostate) and six related cancer subtype classifications (N = 12 total).<h4>Results</h4>Directly modeling isoforms using transcriptome-wide association studies (isoTWAS) "],"journal":["British journal of cancer"],"pubmed_title":["Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level."],"pmcid":["PMC12449480"],"funding_grant_id":["R21 CA293419","R01 CA194393","CA293419","U01 CA194393"],"pubmed_authors":["Chang YH","Pasaniuc B","Harrison TA","Lindstrom S","Taylor Head S","Bhattacharya A","Bresnahan ST","Huff CD","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level.","description":"<h4>Background</h4>Integrating genome-wide association study (GWAS) and transcriptomic datasets can identify mediators for genetic risk of cancer. Traditional methods often are insufficient as they rely on total gene expression measures and overlook alternative splicing, which generates different transcript-isoforms with potentially distinct effects.<h4>Methods</h4>We integrate multi-tissue isoform expression data from the Genotype Tissue-Expression Project with GWAS summary statistics (all N > ~20,000 cases) to identify isoform- and gene-level associations with six cancers (breast, endometrial, colorectal, lung, ovarian, prostate) and six related cancer subtype classifications (N = 12 total).<h4>Results</h4>Directly modeling isoforms using transcriptome-wide association studies (isoTWAS) ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-03T17:29:25.09Z","creation":"2026-04-29T03:13:28.212Z"},"accession":"S-EPMC12449480","cross_references":{"pubmed":["40775447"],"doi":["10.1038/s41416-025-03141-y"]}}