Abstract
While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk.
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Data availability
Full allelic imbalance results and all RWAS model weights are available at https://doi.org/10.5281/zenodo.6371439. ATAC-Seq data for all cancer samples are available at https://gdc.cancer.gov/about-data/publications/ATACseq-AWG. SNP-SELEX assay data is available at http://renlab.sdsc.edu/GVATdb/search.html. SuRE assay data is available at https://osf.io/6wev3/. The hg19 reference genome (human_g1k_v37) can be found at https://www.internationalgenome.org/category/grch37/. GTEx v.8 data can be found at https://www.gtexportal.org/home/datasets. Cancer eQTL data can be found at http://gong_lab.hzau.edu.cn/PancanQTL/. TWAS models can be found at http://gusevlab.org/projects/fusion/.
Code availability
Code to conduct allelic imbalance analyses, build RWAS models and conduct RWAS analyses is available at https://doi.org/10.5281/zenodo.6371678.
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Acknowledgements
We thank M. Freedman, B. Pasaniuc and N. Mancuso for providing feedback on the manuscript. D.G. and A.G. were supported by R01 CA227237 and R01 CA244569. A.G. was also supported by R01 MH115676 and R01 CA259200. D.G. was also supported by the IBM Ph.D. Fellowship Award.
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A.G. conceived and supervised the project and developed RWAS. D.G. conducted all analyses. D.G. and A.G. wrote the manuscript.
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Extended data
Extended Data Fig. 1 Effects of population structure, sample size, peak definitions and CNVs on imbalance analysis.
(a) Inferred genetic ancestry of ATAC-seq samples; each sample is a point color-coded by self-reported race (unfilled circles represent “not reported”) plotted along with the top two genetic Principal Components (PC) shown on the x and y axes. Allelic imbalance is not inflated by population structure due to “canceling out” trans/environmental effects by comparing functional activity at the two alleles within an individual, and only testing heterozygous carriers (thus not biased by population differences in allele frequency). (b) The number of discovered significant as-aQTLs (y-axis) grows linearly with the number of (randomly downsampled) cancer samples (x-axis). (c) The discovery of as-aQTLs does not strongly depend on peak calling with MACS. Many of the same regions are identified without any previous peak calling. (d) The density of as-aQTLs (y-axis) increases exponentially in high-CNV genomic regions (x-axis). The segment mean values are calculated from TCGA somatic copy number calls (segment mean =| log2(CNV/2)|). The chosen maximum threshold of 0.6 corresponds to the gain of one additional allele copy (0.58 ≈| log2(3/2) |).
Extended Data Fig. 2 Cancer as-aQTLs are strongly enriched for cancer risk heritability.
(a) Meta-analysis of 7 cancer types shows that as-aQTLs are more strongly enriched for cancer risk heritability (x-axis) than any other evaluated annotation (y-axis/bars); error bars correspond to the estimated standard error. Data are presented as inverse-variance weighted mean values + /- s.e. (b) as-aQTLs discovered in all 7 cancer types are also enriched for cancer risk variants when the enrichment is quantified using a simpler strategy based on top GWAS associations that does not account for background annotations (see Supplementary Note). Z-scores for the significance of enrichment are shown.
Extended Data Fig. 3 Heritability enrichment at cancer as-aQTLs is cancer type-specific.
(a-c) Cancer type-specific as-aQTLs do not exhibit heritability enrichment for noncancer traits. Data are presented as mean values + /- s.e. (d-e) The cancer risk heritability enrichment at pancancer as-aQTLs is lower than at matching cancer type-specific as-aQTLs. Data are presented as mean values + /- s.e.
Extended Data Fig. 4 Cancer as-aQTLs and d-as-aQTLs are strongly enriched for eQTLs.
(a) The enrichment of cancer-specific accessible peaks (by cancer type: y-axis) for intersecting GTEx tissue eQTLs (by tissue: x-axis) relative to random genomic sequences. Each cell reports the corresponding enrichment (ratio of eQTLs in peaks to eQTLs in random regions) and is shaded by this value. The pancancer peak set (top row) is expected to have a higher fraction of rare/private peaks, which likely explains the apparent lower eQTL enrichment. (b) The fraction of as-aQTLs (by cancer type: y-axis) that intersect one or more GTEx tissue eQTLs (by tissue: x-axis). Each cell reports the fraction and is shaded by this value. For many cancer-tissue pairs, >50% of as-aQTLs contain eQTLs. (c) The enrichment of cancer d-as-aQTLs for intersecting GTEx tissue eQTLs relative to all cancer type-specific peaks. Each cell reports the corresponding enrichment (ratio) and is shaded by this value. Non-significant enrichments (Z < 2, see Methods) are shown as NA. (d) The average Z-scores for the enrichment of cancer as-aQTLs for GTEx tissue eQTLs (averaged across all GTEx tissues, y-axis) as a function of the number of samples in the corresponding cancer type (x-axis). Each point is labeled by the corresponding cancer type. eQTL enrichment z-scores correlate with the number of samples per cancer type.
Extended Data Fig. 5 Distribution of allelic fractions at as-aQTLs with functional and disrupted TF motifs.
(a-l) Violin plots for selected TF motifs representing 12 motif families. The distribution of allelic fractions at as-aQTLs is shown for functional (red) and disrupted (blue) TF motifs.
Extended Data Fig. 6 Additional TF motif scores calculations at as-aQTLs and correlations between as-aQTL allelic fractions and TF binding / gene expression.
(a) Allelic fraction differences (left) and HOMER motif scores (right) from an additional 30 TF motifs for which the difference in motif scores between sequences with high and lower allelic fractions was most significant. Alleles with higher allelic fractions are shown in blue and alleles with lower allelic fractions in orange. Note the reversed directionality for the LRF motif, which is known to act as a repressor of transcription (Constantinou, C. et al. The multi-faceted functioning portrait of LRF/ZBTB7A. Hum. Genomics 13, 66 (2019)). Data are presented as mean values + /- s.d. (b) The correlation between significant SNP-SELEX SNP-TF pair PBS values (p < 0.01) and as-aQTL allelic fractions (y-axis) does not significantly change with varying allelic fraction thresholds |AF-0.5 | (x-axis). Data are presented as Pearson correlations + /- s.e. Number of pairs used for correlation analysis are shown above each point. (c) The correlation between significant SuRE SNP ΔExpressionALT-REF values (p < 0.00173121) and as-aQTL allelic fractions (y-axis) changes only slightly with varying allelic fraction thresholds |AF-0.5 | (x-axis). Data are presented as Pearson correlations + /- s.e. Number of pairs used for correlation analysis are shown above each point.
Extended Data Fig. 7 Additional RWAS analyses.
(a) Numbers of significant RWAS associations between peaks with variants and cancer risk discovered for 7 cancer types using 6 model types. (b) Numbers of significant RWAS associations between peaks without variants and cancer risk discovered for 7 cancer types using 6 model types. In total, 491036 peaks were analyzed of which 337793 (~69%) had a variant and 153243 (~31%) did not. (c) TWAS conducted using GTEx normal tissue expression data yields a comparable number of GWAS risk loci with TWAS genes. The lower number of prostate cancer TWAS genes is due to the smaller sample size of the normal prostate RNA-Seq dataset (132 GTEx samples vs. 468 TCGA samples). (d) RWAS still outperforms TWAS when only top1.total and top1.lasso model types are used (the same model types used for TWAS). (e) The correlations between heritable (cross-validation P of < 0.05) and TWAS-significant genes and RWAS-significant peaks at breast and prostate cancer risk loci are significantly stronger than the correlations between heritable (cross-validation p-values of < 0.05) but non-significant TWAS genes and RWAS peaks at GWAS risk loci. Horizontal lines inside the boxes indicate the medians. Box bounds show Q1 and Q3. Whiskers are minima (Q1 - 1.5x(Q3-Q1)) and maxima (Q3 + 1.5x(Q3-Q1)). (f) The correlation between heritable TWAS genes and RWAS peaks increases with decreasing distance. RWAS peak correlations with TWAS genes identified from expression in normal tissue are not significantly different from correlations with TWAS genes identified from the expression in cancer tissues. Matching TCGA breast normal and cancer samples were used for TWAS. Horizontal lines inside the boxes indicate the medians. Box bounds show Q1 and Q3. Whiskers are minima (Q1 - 1.5x(Q3-Q1)) and maxima (Q3 + 1.5x(Q3-Q1)).
Extended Data Fig. 8 Prostate cancer risk-associated RWAS peaks are linked to risk-associated TWAS genes.
(a) Correlations between TWAS gene and RWAS peak pairs at 33 prostate cancer GWAS risk loci. Nodes representing TWAS genes are shown in red with gene names shown in brackets. Nodes representing RWAS peaks are shown in black. The color of the edges represents the strength of the correlations between models (absolute Pearson correlation). (b) Median absolute Pearson correlations between significant TWAS gene and RWAS peak pairs at each GWAS prostate cancer risk locus. Horizontal lines inside the boxes indicate the medians. Box bounds show Q1 and Q3. Whiskers are minima (Q1 - 1.5x(Q3-Q1)) and maxima (Q3 + 1.5x(Q3-Q1)).
Extended Data Fig. 9 Prostate cancer risk-associated RWAS peaks are linked to risk-associated CWAS features.
(a) Overlap of significant RWAS and CWAS (H3K27ac) peaks with prostate cancer GWAS risk loci and significant RWAS and CWAS (AR) peaks with prostate cancer GWAS risk loci. (b) Correlations between prostate cancer risk-associated RWAS and CWAS peak pairs across all prostate cancer GWAS risk loci are similar to the RWAS-TWAS associations (Extended Data Fig. 8). Horizontal lines inside the boxes indicate the medians. Box bounds show Q1 and Q3. Whiskers are minima (Q1 - 1.5x(Q3-Q1)) and maxima (Q3 + 1.5x(Q3-Q1)). (c) Correlations between CWAS (H3K27ac) and RWAS peak pairs at 42 prostate cancer GWAS risk loci. Horizontal lines inside the boxes indicate the medians. Box bounds show Q1 and Q3. Whiskers are minima (Q1 - 1.5x(Q3-Q1)) and maxima (Q3 + 1.5x(Q3-Q1)). (d) Correlations between CWAS (AR) and RWAS peak pairs at 27 prostate cancer GWAS risk loci. Horizontal lines inside the boxes indicate the medians. Box bounds show Q1 and Q3. Whiskers are minima (Q1 - 1.5x(Q3-Q1)) and maxima (Q3 + 1.5x(Q3-Q1)).
Extended Data Fig. 10 RWAS associations can explain GWAS risk loci.
(a-f) Examples of RWAS-significant peaks near COSMIC genes that explain a large portion of the GWAS signal in conditional analyses. Each dot corresponds to a GWAS SNP with the significance of the association on the y-axis and physical position on the x-axis. The gray dots indicate marginal GWAS signals, and the blue dots show the same signals become less significant after conditioning on an as-aQTL/balanced peak identified in RWAS.
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Grishin, D., Gusev, A. Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms. Nat Genet 54, 837–849 (2022). https://doi.org/10.1038/s41588-022-01075-2
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DOI: https://doi.org/10.1038/s41588-022-01075-2
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