A splicing-based multi-tissue joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Abstract

AbstractSplicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify new susceptibility genes. However, existing splicing-TWASs test association of individual excised introns in breast tissue only and have thus limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 European ancestry women. Splicing level prediction models were trained in GTEx (v8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified 9 additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs and 17 genes in 7 loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our spicing-TWASs with previous gene expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci identified by our splicing-TWASs were not reported in the expression-based TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show significant impact on breast cancer risk, while splicing quantitative trait loci (sQTL) showed strong impact through intron excision events.