Pan-cancer and cross-population genome-wide association studies dissect shared genetic backgrounds underlying carcinogenesis

Abstract

Integrating genomic data of multiple cancers allows de novo cancer grouping and elucidating the shared genetic basis across cancers. Here, we conduct the pan-cancer and cross-population genome-wide association study (GWAS) meta-analysis and replication studies on 13 cancers including 250,015 East Asians (Biobank Japan) and 377,441 Europeans (UK Biobank). We identify ten cancer risk variants including five pleiotropic associations (e.g., rs2076295 at DSP on 6p24 associated with lung cancer and rs2525548 at TRIM4 on 7q22 nominally associated with six cancers). Quantifying shared heritability among the cancers detects positive genetic correlations between breast and prostate cancer across populations. Common genetic components increase the statistical power, and the large-scale meta-analysis of 277,896 breast/prostate cancer cases and 901,858 controls identifies 91 newly genome-wide significant loci. Enrichment analysis of pathways and cell types reveals shared genetic backgrounds across said cancers. Focusing on genetically correlated cancers can contribute to enhancing our insights into carcinogenesis.

Fig. 1. The study overview.

The pan-cancer and cross-population GWAS meta-analysis on 13 cancer types followed by the replication analysis examining 250,015 East Asians from the BioBank Japan (BBJ) and 377,441 Europeans from the UK Biobank (UKB) identified ten loci newly satisfying the genome-wide significance threshold (upper). We then estimated the heritability and genetic correlations among the cancers and found significant positive genetic correlations between breast and prostate cancer both in BBJ and UKB (middle). The breast and prostate cancer large-scale meta-analysis including the FinnGen datasets, and the largest-to-date GWAS datasets of breast (BCAC) and prostate cancer (PRACTICAL) detected 91 newly genome-wide significant loci. Further enrichment analysis of cell types and pathways demonstrated shared genetic backgrounds between the two cancers (bottom).

Fig. 2. Novel loci identified in the all-cancer meta-analysis.

Regional plots of the novel loci and forest plots of the lead loci variants identified in the all-cancer meta-analysis. Purple diamond symbols in the regional plots represent the lead variants of the loci. In the forest plots, dots indicate the odds ratios of the variant for each cancer and whiskers represent 95% confidence intervals. The number of cases and controls in each GWAS are shown in Table 1. All statistical tests are two-sided and not adjusted for multiple comparisons.

Fig. 3. Breast and prostate cancer analysis.

a Forest plot of genetic correlations between breast and prostate cancer in BBJ, UKB, and FinnGen. Dots indicate genetic correlations and whiskers represent 95% confidence intervals. b Heatmap describing the associations between the three GWAS meta-analyses of breast and prostate cancer and the top-ranking gene sets associated with the meta-analysis across breast and prostate cancer. The “Meta” column represents the meta-analysis across breast and prostate cancer. P-values of the heatmap are uncorrected and reflect two-sided tests. FDR was calculated via the Benjamini-Hochberg method across all gene sets. c Results of the cell type-specific analysis. UMAP visualizations of the breast cancer scRNA-seq dataset colored by cell type (top) and disease scores calculated via scDRS (middle). Heatmap describing the associations between the three GWAS meta-analyses of breast and prostate cancer and the cell types detected in the scRNA-seq datasets of breast and prostate cancer (down). The “Meta” column represents the meta-analysis across breast and prostate cancer. P-values of the heatmap are uncorrected and reflect two-sided tests. FDR was calculated via the Benjamini-Hochberg method across all cell types in each scRNA-seq dataset.