Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level

Integrating genome-wide association study (GWAS) and transcriptomic datasets can help identify potential mediators for germline genetic risk of cancer. However, traditional methods have been largely unsuccessful because of an overreliance on total gene expression. These approaches overlook alternative splicing, which can produce multiple isoforms from the same gene, each with potentially different effects on cancer risk.

Here, we integrate genetic and multi-tissue isoform-level gene expression data from the Genotype Tissue-Expression Project (GTEx, N = 108-574) with publicly available European-ancestry GWAS summary statistics (all N > 20,000 cases) to identify both isoform- and gene-level risk associations with six cancers (breast, endometrial, colorectal, lung, ovarian, prostate) and six related cancer subtype classifications (N = 12 total). Compared to traditional methods leveraging total gene expression, directly modeling isoform expression through transcriptome-wide association studies (isoTWAS) substantially increases discovery of transcriptomic mechanisms underlying genetic associations. Using the same RNA-seq datasets, isoTWAS identified 164% more significant unique gene associations compared to TWAS (6,163 and 2,336, respectively), with isoTWAS-prioritized genes enriched 4-fold for evolutionarily-constrained genes (P = 6.1 × 10 ⁻¹³ ). isoTWAS tags transcriptomic associations at 52% more independent GWAS loci compared to TWAS across the six cancers. Additionally, isoform expression mediates an estimated 63% greater proportion of cancer risk SNP heritability compared to gene expression when evaluating cis-genetic influence on isoform expression. We highlight several notable isoTWAS associations that demonstrate GWAS colocalization at the isoform level but not at the gene level, including, CLPTM1L (lung cancer), LAMC1 (colorectal), and BABAM1 (breast). These results underscore the critical importance of modeling isoform-level expression to maximize discovery of genetic risk mechanisms for cancers.