Shared heritability and functional enrichment across six solid cancers

Abstract

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r_g = 0.57, p = 4.6 × 10^-8), breast and ovarian cancer (r_g = 0.24, p = 7 × 10^-5), breast and lung cancer (r_g = 0.18, p =1.5 × 10^-6) and breast and colorectal cancer (r_g = 0.15, p = 1.1 × 10^-4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

Fig. 1

Estimates of SNP-heritability ($h_g^2$) and cross-cancer heritability (r_g) for the six cancer types. SNP-heritability and cross-cancer heritability are calculated based on HapMap3 SNPs using LD score regression (LDSC). a The solid bar represents overall SNP $h_g^2$ on the liability scale, calculated based on all HapMap3 SNPs. The dark green bar represents $h_g^2$ calculated based on non-significant SNPs—the remaining SNPs after excluding genome-wide significant hits (p < 5 × 10⁻⁸) ± 500 kb. The black bar with density texture indicates proportion of $h_g^2$ (as reflected by the percentages displayed on top of each bar) that could be explained by top hits ±500 kb surrounded areas. The orange error bars represent 95% confidence intervals. b The solid blue bar represents overall SNP $h_g^2$ in liability scale (no SNP exclusion), with black error bars indicating 95% confidence intervals. The red short lines correspond to classical estimates of h² measured in a twin study of Scandinavian countries (Mucci et al.). c Genetic correlations between cancers. Estimates withstood Bonferroni corrections (p < 0.05/15) are marked with double asterisk (**), and nominal significant results (p < 0.05) are marked with single asterisk (*)

Fig. 2

Local genetic correlation between breast, lung and prostate cancer. The region-specific p-values for the local genetic covariance for breast and prostate cancer are shown in a, and for lung and prostate cancer in b. Each dot presents a specific genomic region. In the QQ plots, red color indicates significance after multiple corrections (p < 0.05/1703 regions compared), and blue color indicates nominal significance (p < 0.05/15 pairs of cancers compared). Manhattan-style plots showing the estimates of local genetic covariance for breast and prostate cancer (c), and for lung and prostate cancer (d). Although breast and prostate cancer only show modest genome-wide genetic correlation, two loci exhibit significant local genetic covariance. Similarly, albeit the negligible overall genetic correlation for lung and prostate cancer, three loci present significant local genetic covariance. In the Manhattan plots, red color indicates even number chromosomes and blue color indicates odd number chromosomes

Fig. 3

Cross-trait genetic correlation (r_g) analysis between cancers and non-cancer traits. The traits were divided into four categories: a Common phenotypes, b Metabolic or cardiovascular related traits, c Psychiatric traits, d Autoimmune inflammatory diseases. Pair-wise genetic correlations withstood Bonferroni corrections (228 tests) are marked with double asterisk (**), with estimates of correlation shown in the cells. Pair-wise genetic correlations with significance at p < 0.01 are marked with a single asterisk (*). The color of cells represents the magnitude of correlation

Fig. 4

Putative directional relationships between cancers and traits. For each cancer–trait pair identified as candidates to be related in a causal manner, the plots show trait-specific effect sizes (beta coefficients) of the included genetic variants. Gray lines represent the relevant standard errors. a HDL and breast cancer. Trait-specific effect sizes for HDL and breast cancer are shown for SNPs associated with HDL levels (left) and breast cancer (right). b Schizophrenia and breast cancer. Trait-specific effect sizes for schizophrenia and breast cancer are shown for SNPs associated with schizophrenia (left) and breast cancer (right). c Age at natural menopause and breast cancer. Trait-specific effect sizes for age at natural menopause and breast cancer are shown for SNPs associated with age at natural menopause (left) and breast cancer (right). d Lupus and prostate cancer. Trait-specific effect sizes for lupus and prostate cancer are shown for SNPs associated with lupus (left) and prostate cancer (right)

Fig. 5

Enrichment p-values of 24 non-cell-type-specific functional categories over six cancer types. The x-axis represents each of the 24 functional categories, y-axis represents log-transformed p-values of enrichment. Annotations with statistical significance after Bonferroni corrections (p < 0.05/24) were plotted in orange, otherwise blue. The horizontal gray dash line indicates p-threshold of 0.05; horizontal red dash line indicates p-threshold of 0.05/24. From top to bottom are six panels representing six cancers: breast cancer, colorectal cancer, head/neck cancer, lung cancer, ovarian cancer, and prostate cancer. TSS transcription start site, UTR untranslated region, TFBS transcription factor binding sites, DHS DNase I hypersensitive sites, DGF digital genomic foot printing, CTCF CCCTC-binding factor