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Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Nature Genetics volume 54, pages 1364–1375 (2022)Cite this article

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Abstract

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

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Fig. 1: Overview of the method.
Fig. 2: Genetic variation creates abundant cQTLs and allelically imbalanced regulatory elements.
Fig. 3: Integrative cistrome models identify genetic determinants of gene regulation.
Fig. 4: CWAS identifies prostate cancer risk mediated by genetic variation in AR binding and regulatory element activity.
Fig. 5: CWAS identifies associations not marked by a steady-state eQTL.
Fig. 6: CWAS associations linked to selected prostate developmental genes and proto-oncogenes.
Fig. 7: CWAS identifies ARBS underlying heritability of multiple androgen-regulated phenotypes.

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Data availability

Public datasets used in this study are listed in Supplementary Table 1. Data generated for this study are available in GEO (accession number GSE205885).

Code availability

Scripts to reproduce analyses from this study are available at https://github.com/scbaca/cwas, https://github.com/scbaca/chip_imputation and https://doi.org/10.5281/zenodo.6666796.

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Acknowledgements

This work is supported by grants from the PhRMA Foundation and the Kure It Cancer Research Foundation (S.C.B.). The androgen deprivation GWAS was supported in part by the National Cancer Institute of the National Institutes of Health under award numbers U10CA180820, U10CA180794 and UG1CA233180 (C.J.S.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We are grateful to the E3805: CHAARTED investigators; the patients who participated in the trial; the Prostate Cancer Foundation Mazzone Awards; and Sanofi for partial financial support and supplying docetaxel for early use (C.J.S.). In addition, we acknowledge Public Health Service grants CA180794, CA180820, CA23318, CA66636, CA21115, CA49883, CA16116, CA21076, CA27525, CA13650, CA14548, CA35421, CA32102, CA31946, CA04919, CA107868 and CA184734 (C.J.S.). We are grateful for the generous support of Rebecca and Nathan Milikowsky and Debbie and Bob First (S.C.B.).

Author information

Author notes

  1. These authors jointly supervised this work: Alexander Gusev, Matthew L. Freedman.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Sylvan C. Baca, Soumya Zacharia, Ji-Heui Seo, André P. Fay, Cynthia Kalita, Stefan Groha, Mark M. Pomerantz, Christopher J. Sweeney, Alexander Gusev & Matthew L. Freedman

  2. Center for Functional Cancer Epigenetics Dana-Farber Cancer Institute, Boston, MA, USA

    Sylvan C. Baca, Soumya Zacharia, Ji-Heui Seo, Mark M. Pomerantz & Matthew L. Freedman

  3. The Eli and Edythe L. Broad Institute, Cambridge, MA, USA

    Sylvan C. Baca, Stefan Groha, Alexander Gusev & Matthew L. Freedman

  4. Laboratory of Genitourinary Cancer Pathogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Cassandra Singler & David Y. Takeda

  5. Vancouver Prostate Centre University of British Columbia, Vancouver, British Columbia, Canada

    Tunc Morova, Faraz Hach, Chia-Chi Flora Huang & Nathan A. Lack

  6. Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA

    Yi Ding, Tommer Schwarz & Bogdan Pasaniuc

  7. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

    Jacob Anderson

  8. Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA

    Cynthia Kalita & Alexander Gusev

  9. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Victoria Wang

  10. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Victoria Wang

  11. Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands

    Simon Linder & Wilbert Zwart

  12. Department of Biomedical Engineering, Laboratory of Chemical Biology and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands

    Simon Linder & Wilbert Zwart

  13. School of Medicine, Koç University, Istanbul, Turkey

    Nathan A. Lack

  14. Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Bogdan Pasaniuc

  15. Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Bogdan Pasaniuc

  16. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Bogdan Pasaniuc

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  1. Sylvan C. Baca
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  2. Cassandra Singler
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  3. Soumya Zacharia
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  14. Mark M. Pomerantz
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  15. Victoria Wang
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  16. Simon Linder
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  17. Christopher J. Sweeney
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  19. Nathan A. Lack
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  20. Bogdan Pasaniuc
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  21. David Y. Takeda
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  22. Alexander Gusev
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  23. Matthew L. Freedman
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Contributions

S.C.B., A.G. and M.L.F. conceived the study. S.C.B. analyzed the data and wrote the manuscript under the joint supervision of M.L.F. and A.G. A.F. performed ChIP–seq experiments. J.-H.S. generated and C.K. analyzed allelic imbalance data from LNCaP cells. T.M. and Y.D. analyzed SNP STARR-seq data under the supervision of N.L. and B.P. C.S. performed CRISPRi experiments under the supervision of D.Y.T. S. Z. assisted with analysis of ChIP–seq data. S.G. assisted with genotype imputation. S.L. and W.Z. provided prostate cancer ChIP–seq data. M.M.P. and V.W. analyzed ADT GWAS data performed on samples and data provided by C.J.S. J.A. assisted with implementation of the CWAS pipeline.

Corresponding authors

Correspondence to Alexander Gusev or Matthew L. Freedman.

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Extended data

Extended Data Fig. 1 Accurate genotyping of SNPs from epigenomic data.

(a) Overview of 575 epigenomic datasets merged across 163 individuals for genotyping. Datasets are colored by cohort (See Supplementary Table 1). (b) Genomic distribution of reads in ChIP-seq, RNA-seq and input control (whole genome) data. The genome was divided into non-overlapping 500 base-pair windows and cumulative read counts for each bin were summed. For each datatype, five samples were randomly selected and down-sampled to 8.4 million reads for uniformity. The mean percentage of bins with the indicated number of read counts is shown for each datatype. (c) Number of covered SNPs (≥ 5 reads) versus total aggregated reads for each individual. (d) Number of covered SNPs (≥ 5 reads) for each individual (n = 165) as the indicated number of datasets are merged. Datasets were added in random order for a given individual. For boxplots, lower and upper hinges indicate 25th and 75th percentiles; whiskers extend to 1.5 x the inter-quartile ranges (IQR). (e) Correlation of imputed versus array-based genotype dosages across 24 individuals. (f) Receiver operating characteristic curve for detection of heterozygous SNPs using sequencing and imputation, with array-based genotypes as ground truth. Dotted red line indicates a mean sensitivity of 0.92 at a specificity of 0.9 in individuals of European ancestry.

Extended Data Fig. 2 Inferred ancestry of individuals in the study.

Projection of imputed genotypes onto the first two principal components of continental ancestry from ref. 78. Individual identifiers for outlier samples (with values > 2 x standard deviation) are labeled. Self-reported ancestry is coded by color.

Extended Data Fig. 3 Overlap of cQTLs with prostate tissue eQTLs.

(a) Enrichment of genetically determined AR peaks (left) and H3K27ac peaks (right) for overlap with GWAS risk SNPs eQTLs across various tissues. Empiric p values are derived 10,000 from permutations. (b) number of AR and H3K27ac cQTLs that are also the top eQTL for a gene in prostate tissue. (c) correlation of cQTL and eQTL effect size (β) for cQTL SNPs; p-value for Pearson correlation test is indicated. (d) Examples of SNPs (labeled with rs identifier) that are both AR cQTLs and eQTLs where the corresponding cPeak and eGene are connected by an H3K27ac HiChIP loop in LNCaP. cPeak coordinates are shown and eGene transcriptional start sites (TSS) is denoted. (e) Contingency table showing enrichment of H3K27ac HiChIP looping between the corresponding cPeak and eGene for cQTLs that are also eQTLs. Chi-square test p-values are indicated.

Extended Data Fig. 4 Distribution of cQTLs around cPeaks.

cQTL SNP significance versus distance to the center of the corresponding cPeak for significant cQTLs (permutation-based q-value < 0.05). Dashed blue lines indicate ± 25 Kb from the peak center.

Extended Data Fig. 5 Conditioning of GWAS SNP significance on genetically predicted CWAS AR binding.

Genomic context of AR CWAS ARBS (depicted in green) that are significantly associated with prostate cancer risk. Manhattan plots indicate significance of SNP associations with prostate cancer before and after conditioning on genetically predicted CWAS ARBS activity. (a) and (b) show representative examples where ARBS explain most of the nearby cis-SNP GWAS significance. (c) CWAS ARBS at the promoter of GGCX, where residual GWAS significance remains after conditioning on ARBS, suggesting additional mechanisms underlying risk conferred by SNPs in this region.

Extended Data Fig. 6 Comparison of CWAS and GWAS significance for tested ARBS and H3K27ac peaks.

The absolute value of the association Z-score is plotted for CWAS peak-trait associations (y-axis) and GWAS SNP-trait associations for the most significant nearby SNP (x-axis). (a) shows ARBS and (b) shows H3K27ac peaks. Dashed horizontal lines indicate genome-wide significance thresholds for CWAS. Vertical dotted lines indicate the GWAS significance threshold of z = 5.45.

Extended Data Fig. 7 Enrichment of prostate cancer GWAS risk SNPs in genetically determined AR peaks and H3K27ac peaks.

Enrichment and p-values for AR peaks (a) and H3K27ac peaks (b) derived from linkage disequilibrium score regression5.

Extended Data Fig. 8 cQTL vs. eQTL activity at TMPRSS2 and NKX3-1 loci.

(a) Normalized AR ChIP-seq reads at the TMPRSS2 enhancer and TMPRSS2 expression stratified by genotype of the indicated SNP. (b) Normalized H3K27ac ChIP-seq reads at the NKX3-1 enhancer and NKX3-1 expression stratified by genotype of the indicated SNP. ρ and p-values indicate Pearson correlation coefficient for (A) and (B). (c) Estimated cis-SNP heritability for the indicated epigenomic features and corresponding genes. For boxplots, lower and upper hinges indicate 25th and 75− percentiles; whiskers extend to 1.5 x the inter-quartile ranges (IQR).

Supplementary information

Supplementary Information

Supplementary Methods and Supplementary Note.

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Supplementary Tables

Supplementary Tables 1–11.

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Baca, S.C., Singler, C., Zacharia, S. et al. Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation. Nat Genet 54, 1364–1375 (2022). https://doi.org/10.1038/s41588-022-01168-y

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