Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Gene regulatory mechanisms underpinning prostate cancer susceptibility

Nature Genetics volume 48, pages 387–397 (2016)Cite this article

Subjects

Abstract

Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Identification of regulatory SNPs and eQTL genes at prostate cancer risk loci.
Figure 2: SNPs disrupt binding of AR-FOXA1 complexes at prostate cancer risk loci.
Figure 3: rs4919743 disrupts tumor-specific AR-FOXA1 binding and expression of KRT8.
Figure 4: LD proxy SNPs for prostate cancer risk alter diverse gene regulatory mechanisms.
Figure 5: aseQTL validation of regulatory SNPs and target genes.
Figure 6: Integrated visualization of eQTL and CRM annotation data.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Schaub, M.A., Boyle, A.P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Spitz, F. & Furlong, E.E.M. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Panne, D., Maniatis, T. & Harrison, S.C. An atomic model of the interferon-β enhanceosome. Cell 129, 1111–1123 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Arnosti, D.N., Barolo, S., Levine, M. & Small, S. The eve stripe 2 enhancer employs multiple modes of transcriptional synergy. Development 122, 205–214 (1996).

    CAS  PubMed  Google Scholar 

  7. Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Johnson, D.S., Mortazavi, A., Myers, R.M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Whitington, T., Frith, M.C., Johnson, J. & Bailey, T.L. Inferring transcription factor complexes from ChIP-seq data. Nucleic Acids Res. 39, e98 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jankowski, A., Szczurek, E., Jauch, R., Tiuryn, J. & Prabhakar, S. Comprehensive prediction in 78 human cell lines reveals rigidity and compactness of transcription factor dimers. Genome Res. 23, 1307–1318 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Smith, R.P. et al. Massively parallel decoding of mammalian regulatory sequences supports a flexible organizational model. Nat. Genet. 45, 1021–1028 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Al Olama, A.A. et al. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 46, 1103–1109 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Huang, Q. et al. A prostate cancer susceptibility allele at 6q22 increases RFX6 expression by modulating HOXB13 chromatin binding. Nat. Genet. 46, 126–135 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Zhang, X., Cowper-Sallari, R., Bailey, S.D., Moore, J.H. & Lupien, M. Integrative functional genomics identifies an enhancer looping to the SOX9 gene disrupted by the 17q24.3 prostate cancer risk locus. Genome Res. 22, 1437–1446 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Grisanzio, C. et al. Genetic and functional analyses implicate the NUDT11, HNF1B, and SLC22A3 genes in prostate cancer pathogenesis. Proc. Natl. Acad. Sci. USA 109, 11252–11257 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xu, X. et al. Variants at IRX4 as prostate cancer expression quantitative trait loci. Eur. J. Hum. Genet. 22, 558–563 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Li, Q. et al. Expression QTL–based analyses reveal candidate causal genes and loci across five tumor types. Hum. Mol. Genet. 23, 5294–5302 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Penney, K.L. et al. Association of prostate cancer risk variants with gene expression in normal and tumor tissue. Cancer Epidemiol. Biomarkers Prev. 24, 255–260 (2015).

    Article  CAS  PubMed  Google Scholar 

  19. Hazelett, D.J. et al. Comprehensive functional annotation of 77 prostate cancer risk loci. PLoS Genet. 10, e1004102 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Jia, L. et al. Functional enhancers at the gene-poor 8q24 cancer-linked locus. PLoS Genet. 5, e1000597 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Liu, T. et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 12, R83 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wu, D. et al. Three-tiered role of the pioneer factor GATA2 in promoting androgen-dependent gene expression in prostate cancer. Nucleic Acids Res. 42, 3607–3622 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Battle, A. et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14–24 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).

    Article  CAS  PubMed  Google Scholar 

  25. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).

  26. Pickrell, J.K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Dixon, J.R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  29. Mills, I.G. Maintaining and reprogramming genomic androgen receptor activity in prostate cancer. Nat. Rev. Cancer 14, 187–198 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Norris, J.D. et al. The homeodomain protein HOXB13 regulates the cellular response to androgens. Mol. Cell 36, 405–416 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pomerantz, M.M. et al. The androgen receptor cistrome is extensively reprogrammed in human prostate tumorigenesis. Nat. Genet. 47, 1346–1351 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jin, H.-J., Zhao, J.C., Wu, L., Kim, J. & Yu, J. Cooperativity and equilibrium with FOXA1 define the androgen receptor transcriptional program. Nat. Commun. 5, 3972 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Melton, C., Reuter, J.A., Spacek, D.V. & Snyder, M. Recurrent somatic mutations in regulatory regions of human cancer genomes. Nat. Genet. 47, 710–716 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tomlins, S.A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Nguyen, H.H. et al. IRX4 at 5p15 suppresses prostate cancer growth through the interaction with vitamin D receptor, conferring prostate cancer susceptibility. Hum. Mol. Genet. 21, 2076–2085 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. Bauer, D.E. et al. An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. Science 342, 253–257 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhou, Y., Bolton, E.C. & Jones, J.O. Androgens and androgen receptor signaling in prostate tumorigenesis. J. Mol. Endocrinol. 54, R15–R29 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. Tuupanen, S. et al. The common colorectal cancer predisposition SNP rs6983267 at chromosome 8q24 confers potential to enhanced Wnt signaling. Nat. Genet. 41, 885–890 (2009).

    Article  CAS  PubMed  Google Scholar 

  40. Feng, J. et al. A genome-wide survey over the ChIP-on-chip identified androgen receptor–binding genomic regions identifies a novel prostate cancer susceptibility locus at 12q13.13. Cancer Epidemiol. Biomarkers Prev. 20, 2396–2403 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hazelett, D.J., Coetzee, S.G. & Coetzee, G.A. A rare variant, which destroys a FoxA1 site at 8q24, is associated with prostate cancer risk. Cell Cycle 12, 379–380 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zaret, K.S. & Carroll, J.S. Pioneer transcription factors: establishing competence for gene expression. Genes Dev. 25, 2227–2241 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Pedersen, S.F. & Stock, C. Ion channels and transporters in cancer: pathophysiology, regulation, and clinical potential. Cancer Res. 73, 1658–1661 (2013).

    Article  CAS  PubMed  Google Scholar 

  44. Yokoyama, Y. et al. Matrilysin (MMP-7) is a novel broadly expressed tumor antigen recognized by antigen-specific T cells. Clin. Cancer Res. 14, 5503–5511 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Szarvas, T. et al. Elevated serum matrix metalloproteinase 7 levels predict poor prognosis after radical prostatectomy. Int. J. Cancer 128, 1486–1492 (2011).

    Article  CAS  PubMed  Google Scholar 

  46. Moll, R., Divo, M. & Langbein, L. The human keratins: biology and pathology. Histochem. Cell Biol. 129, 705–733 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  50. Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  54. Hartmann, H., Guthöhrlein, E.W., Siebert, M., Luehr, S. & Söding, J. P-value–based regulatory motif discovery using positional weight matrices. Genome Res. 23, 181–194 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bryne, J.C. et al. JASPAR, the open access database of transcription factor–binding profiles: new content and tools in the 2008 update. Nucleic Acids Res. 36, D102–D106 (2008).

    Article  CAS  PubMed  Google Scholar 

  56. Newburger, D.E. & Bulyk, M.L. UniPROBE: an online database of protein binding microarray data on protein-DNA interactions. Nucleic Acids Res. 37, D77–D82 (2009).

    Article  CAS  PubMed  Google Scholar 

  57. Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Sonnhammer, E.L., Eddy, S.R., Birney, E., Bateman, A. & Durbin, R. Pfam: multiple sequence alignments and HMM-profiles of protein domains. Nucleic Acids Res. 26, 320–322 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Siepel, A. et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 15, 1034–1050 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Warren, A.Y. et al. Method for sampling tissue for research which preserves pathological data in radical prostatectomy. Prostate 73, 194–202 (2013).

    Article  PubMed  Google Scholar 

  63. Lindberg, J. et al. Exome sequencing of prostate cancer supports the hypothesis of independent tumour origins. Eur. Urol. 63, 347–353 (2013).

    Article  CAS  PubMed  Google Scholar 

  64. Ross-Adams, H. et al. Integration of copy number and transcriptomics provides risk stratification in prostate cancer: a discovery and validation cohort study. EBioMedicine 2, 1133–1144 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Dunning, M.J., Smith, M.L., Ritchie, M.E. & Tavaré, S. beadarray: R classes and methods for Illumina bead-based data. Bioinformatics 23, 2183–2184 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. Eeles, R.A. et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat. Genet. 45, 385–391 e1–e2 (2013).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the authors of all the ChIP-seq data used in this study; a full listing of the original publications and corresponding accession codes is provided in Supplementary Table 1. We acknowledge the ENCODE Consortium and the Stamatoyannopoulos laboratory for generating the DNase-seq data considered here. The RNA-seq data analyzed here were generated by the TCGA Research Network (see URLs). Computations were performed using resources provided by the Swedish National Infrastructure for Computing (SNIC), through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under project b2013051. We acknowledge the National Cancer Research Institute (National Institute of Health Research (NIHR)) collaborative study Prostate Cancer: Mechanisms of Progression and Treatment (ProMPT) collaborative (grant G0500966/75466) (see URLs) and the Addenbrooke's Hospital Human Research Tissue bank, NIHR Cambridge Biomedical Research Centre, which funded tissue collections in Cambridge. This work was funded by a Cancer Research UK program grant, C5047/A14835, awarded to D.E.N., Swedish Cancer Society grants CAN 2012/823 and 09-0677, a Linnaeus grant (contract 70867902), Swedish Research Council grant 2010/3674 and the Cancer Risk Prediction (CRisP) Center (see URLs). Academy of Finland (284618 and 279760) and Jane and Aatos Erkko Foundation grants were awarded to G.-H.W., and a Chinese Scholarship Council fellowship (201206300074) was awarded to P.G.

Author information

Author notes

  1. Thomas Whitington and Ping Gao: These authors contributed equally to this work.

  2. Johan Lindberg, Gong-Hong Wei and Fredrik Wiklund: These authors jointly supervised this work.

Authors and Affiliations

  1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Thomas Whitington, Daniel Klevebring, Robert Karlsson, Henrik Grönberg, Johan Lindberg & Fredrik Wiklund

  2. Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland

    Ping Gao, Wei Song, Yuehong Yang, Ilaria Svezia & Gong-Hong Wei

  3. Biocenter Oulu, University of Oulu, Oulu, Finland

    Ping Gao, Wei Song, Yuehong Yang, Ilaria Svezia & Gong-Hong Wei

  4. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

    Helen Ross-Adams, Alastair D Lamb, Silvia Halim & Mark J Dunning

  5. Department of Urology, Addenbrooke's Hospital, Cambridge, UK

    Alastair D Lamb

  6. Prostate Cancer Research Group, Centre for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo and Oslo University Hospital, Oslo, Norway

    Ian G Mills

  7. Department of Molecular Oncology, Institute of Cancer Research, Oslo University Hospital, Oslo, Norway

    Ian G Mills

  8. Prostate Cancer UK/Movember Centre of Excellence for Prostate Cancer Research, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK

    Ian G Mills

  9. Cancer Research UK Beatson Institute, Glasgow, UK

    Silvia Halim

  10. Department of Pathology and Cytology, Karolinska University Hospital, Stockholm, Sweden

    Lars Egevad

  11. Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden

    Lars Egevad

  12. Department of Pathology, Addenbrooke's Hospital, Cambridge, UK

    Anne Y Warren

  13. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK

    David E Neal

Authors
  1. Thomas Whitington
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ping Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Helen Ross-Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alastair D Lamb
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuehong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ilaria Svezia
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daniel Klevebring
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ian G Mills
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Robert Karlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Silvia Halim
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mark J Dunning
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lars Egevad
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Anne Y Warren
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. David E Neal
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Henrik Grönberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Johan Lindberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Gong-Hong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Fredrik Wiklund
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.W., F.W., G.-H.W. and J.L. conceived the study. T.W. performed bioinformatic analyses following raw data processing and wrote the manuscript with assistance from F.W., G.-H.W., J.L. and P.G. P.G. performed most wet-lab experiments, with assistance from W.S., Y.Y. and I.S., under the supervision of G.-H.W. H.R.-A. and A.D.L. performed sample collection, annotation and curation for the Cambridge prostate tumor cohort, led by D.E.N. J.L. performed sample collection and curation for the Stockholm prostate tumor cohort. L.E. and A.Y.W. provided the pathological data for the Stockholm and Cambridge cohorts, respectively. M.J.D. and S.H. performed gene expression and genotype bioinformatic data processing, respectively, on the in-house prostate tumor data. D.K. performed bioinformatics data processing of RNA-seq data. R.K. assisted with bioinformatics analysis of genetics data. I.G.M. assisted with data interpretation and the coordination of in-house prostate tumor cohort data generation. D.E.N. and H.G. performed a clinical lead role. All authors critically revised the manuscript.

Corresponding author

Correspondence to Thomas Whitington.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–24, Supplementary Tables 1 and 4, and Supplementary Note. (PDF 29675 kb)

Supplementary Tables

Supplementary Tables 2, 3 and 5–10 (XLSX 112 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Whitington, T., Gao, P., Song, W. et al. Gene regulatory mechanisms underpinning prostate cancer susceptibility. Nat Genet 48, 387–397 (2016). https://doi.org/10.1038/ng.3523

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3523

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research