DNA methylation and cis-regulation of gene expression by prostate cancer risk SNPs

English version

Autoři: James Y. Dai ^aff001; Xiaoyu Wang ^aff001; Bo Wang ^aff003; Wei Sun ^aff001; Kristina M. Jordahl ^aff001; Suzanne Kolb ^aff001; Yaw A. Nyame ^aff001; Jonathan L. Wright ^aff001; Elaine A. Ostrander ^aff005; Ziding Feng ^aff001; Janet L. Stanford ^aff001
Působiště autorů: Division of Public Health Sciences, Fred Hutchison Cancer Research Center, Seattle, Washington, United States of America ^aff001; Department of Biostatistics, University of Washington School of Public Health, Seattle, Washington, United States of America ^aff002; Department of Laboratory Medicine, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China ^aff003; Department of Urology, University of Washington School of Medicine, Seattle, Washington, United States of America ^aff004; Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland, United States of America ^aff005; Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington, United States of America ^aff006
Vyšlo v časopise: DNA methylation and cis-regulation of gene expression by prostate cancer risk SNPs. PLoS Genet 16(3): e32767. doi:10.1371/journal.pgen.1008667
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008667

Souhrn

Genome-wide association studies have identified more than 100 SNPs that increase the risk of prostate cancer (PrCa). We identify and compare expression quantitative trait loci (eQTLs) and CpG methylation quantitative trait loci (meQTLs) among 147 established PrCa risk SNPs in primary prostate tumors (n = 355 from a Seattle-based study and n = 495 from The Cancer Genome Atlas, TCGA) and tumor-adjacent, histologically benign samples (n = 471 from a Mayo Clinic study). The role of DNA methylation in eQTL regulation of gene expression was investigated by data triangulation using several causal inference approaches, including a proposed adaptation of the Causal Inference Test (CIT) for causal direction. Comparing eQTLs between tumors and benign samples, we show that 98 of the 147 risk SNPs were identified as eQTLs in the tumor-adjacent benign samples, and almost all 34 eQTL identified in tumor sets were also eQTLs in the benign samples. Three lines of results support the causal role of DNA methylation. First, nearly 100 of the 147 risk SNPs were identified as meQTLs in one tumor set, and almost all eQTLs in tumors were meQTLs. Second, the loss of eQTLs in tumors relative to benign samples was associated with altered DNA methylation. Third, among risk SNPs identified as both eQTLs and meQTLs, mediation analyses suggest that over two-thirds have evidence of a causal role for DNA methylation, mostly mediating genetic influence on gene expression. In summary, we provide a comprehensive catalog of eQTLs, meQTLs and putative cancer genes for known PrCa risk SNPs. We observe that a substantial portion of germline eQTL regulatory mechanisms are maintained in the tumor development, despite somatic alterations in tumor genome. Finally, our mediation analyses illuminate the likely intermediary role of CpG methylation in eQTL regulation of gene expression.

Klíčová slova:

DNA methylation – Gene expression – Gene regulation – Histology – Prostate cancer – Prostate gland – Variant genotypes – Benign tumors

Zdroje

1. Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J. Clin. 2008; 66: 7–30.

2. Cuzick J, Thorat MA, Andriole G, Brawley OW, Brown PH, Culig Z, et al. Prevention and early detection of prostate cancer. Lancet Oncol. 2014; 15: e484–492. doi: 10.1016/S1470-2045(14)70211-6 25281467

3. Stanford JL, Ostrander EA. Familial prostate cancer. Epidemiol. Rev. 2011; 23: 19–23.

4. Ghadirian P, Howe GR, Hislop TG, Maisonneuve P. Family history of prostate cancer: a multi-center case-control study in Canada. Int. J. Cancer. 1997; 70: 679–681. doi: 10.1002/(sici)1097-0215(19970317)70:6<679::aid-ijc9>3.0.co;2-s 9096649

5. Grönberg H, Damber L, Damber JE. Familial prostate cancer in Sweden: a nationwide register cohort study. Cancer. 1996; 77: 138–143. doi: 10.1002/(SICI)1097-0142(19960101)77:1<138::AID-CNCR23>3.0.CO;2-5

6. Matikaine MP, Pukkala E, Schleutker J, Tammela TL, Koivisto P, Sankila R, et al. Relatives of prostate cancer patients have an increased risk of prostate and stomach cancers: a population-based, cancer registry study in Finland. Cancer Causes Control. 2001; 12: 223–230. doi: 10.1023/a:1011283123610 11405327

7. Lichtenstein P, Holm NV, Verkasalo PK, Lliadou A, Kaprio J, Koskenvuo M, et al. Environmental and heritable factors in the causation of cancer-—analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med. 2000; 343: 78–85. doi: 10.1056/NEJM200007133430201

8. Hjelmborg JB, Scheike T, Holst K, Skytthe A, Penney KL, Graff RE, et al. The heritability of prostate cancer in the Nordic twin study of cancer. Cancer Epidemiol Biomarkers Prev. 2014; 23: 2303–2310. doi: 10.1158/1055-9965.EPI-13-0568 24812039

9. Benafif S, Kote-Jarai Z, Eeles RA, PRACTICAL Consortium. A review of prostate cancer genome-wide association studies (GWAS). Cancer Epidemiol Biomarkers Prev. 2018; 27: 845–857. doi: 10.1158/1055-9965.EPI-16-1046 29348298

10. Eeles RA, Olama AA, Benlloch S, Saunders EJ, Leongamornlert DA, Tymrakiewicz M, et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat. Genet. 2013; 45: 385–391. doi: 10.1038/ng.2560 23535732

11. Al Olama AA, Kote-Jarai Z, Berndt SI, Conti DV, Schumacher F, Han Y, et al. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 2014; 46: 1103–1109. doi: 10.1038/ng.3094 25217961

12. Al Olama AA, Kote-Jarai Z, Giles GG, Guy M, Morrison J, Severi G, et al. Multiple loci on 8q24 associated with prostate cancer susceptibility. Nat. Genet. 2019; 41: 1058–1060.

13. Amundadottir LT, Sulem P, Gudmundsson J, Helgason A, Baker A, et al. A common variant associated with prostate cancer in European and African populations. Nat. Genet. 2006; 38: 652–658. doi: 10.1038/ng1808 16682969

14. Eeles RA, Kote-Jarai Z, Al Olama AA, Giles GG, Guy M, Severi G, et al. Identification of seven new prostate cancer susceptibility loci through a genome-wide association study. Nat. Genet. 2009; 41: 1116–1121. doi: 10.1038/ng.450 19767753

15. Eeles RA, Kote-Jarai Z, Giles GG, Olama AA, Guy M, Jugurnauth SK, et al. Multiple newly identified loci associated with prostate cancer susceptibility. Nat. Genet. 2008; 40: 316–321. doi: 10.1038/ng.90 18264097

16. Gudmundsson J, Sulem P, Gudbjartsson DF, Blondal T, Gylfason A, Agnarsson BA, et al. Genome-wide association and replication studies identify four variants associated with prostate cancer susceptibility. Nat. Genet. 2009; 41: 1122–1126. doi: 10.1038/ng.448 19767754

17. Gudmundsson J, Sulem P, Manolescu A, Amundadottir LT, Gudbjartsson D, Helgason A, et al. Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat. Genet. 2007; 39: 631–637. doi: 10.1038/ng1999 17401366

18. Gudmundsson J, Sulem P, Rafnar T, Bergthorsson JT, Manolescu A, Gudbjartsson D, et al. Common sequence variants on 2p15 and Xp11.22 confer susceptibility to prostate cancer. Nat. Genet. 2008; 40: 281–283. doi: 10.1038/ng.89 18264098

19. Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thorleifsson G, Manolescu A, et al. Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat. Genet. 2007, 39: 977–983. doi: 10.1038/ng2062 17603485

20. Haiman CA, Chen GK, Blot WJ, Strom SS, Berndt SI, Kittles RA, et al. Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21. Nat. Genet. 2011; 43: 570–573. doi: 10.1038/ng.839 21602798

21. Kote-Jarai Z, Olama AA, Giles GG, Severi G, Schleutker J, Weischer M, et al. Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study. Nat. Genet. 2011; 43: 785–791. doi: 10.1038/ng.882 21743467

22. Schumacher FR, Berndt SI, Siddiq A, Jacobs KB, Wang Z, Lindstrom S, et al. Genome-wide association study identifies new prostate cancer susceptibility loci. Hum. Mol. Genet. 2011; 20: 3867–3875. doi: 10.1093/hmg/ddr295 21743057

23. Sun J, Zheng SL, Wiklund F, Isaacs SD, Purcell LD, Gao Z, et al. Evidence for two independent prostate cancer risk-associated loci in the HNF1B gene at 17q12. Nat. Genet. 2008; 40: 1153–1155. doi: 10.1038/ng.214 18758462

24. Takata R, Akamatsu S, Kubo M, Takahashi A, Hosono N, Kawaguchi T, et al. Genome-wide association study identifies five new susceptibility loci for prostate cancer in the Japanese population. Nat. Genet. 2010; 42: 751–754. doi: 10.1038/ng.635 20676098

25. Thomas G, Jacobs KB, Yeager M, Kraft P, Wacholder S, Orr N, et al. Multiple loci identified in a genome-wide association study of prostate cancer. Nat. Genet. 2008; 40: 310–315. doi: 10.1038/ng.91 18264096

26. Yeager M, Orr N, Hayes RB, Jacobs KB, Kraft P, Wacholder S, et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat. Genet. 2007; 39: 645–649. doi: 10.1038/ng2022 17401363

27. Duggan D, Zheng SL, Knowlton M, Benitez D, Dimitrov L, Wiklund F, et al. Two genome-wide association studies of aggressive prostate cancer implicate putative prostate tumor suppressor gene DAB2IP. J. Natl. Cancer Inst. 2007; 99: 1836–1844. doi: 10.1093/jnci/djm250 18073375

28. Amin Al Olama A, Kote-Jarai Z, Schumacher FR, Wiklund F, Berndt SI, Benlloch S, et al. A meta-analysis of genome-wide association studies to identify prostate cancer susceptibility loci associated with aggressive and non-aggressive disease. Hum. Mol. Genet. 2013; 22: 408–415. doi: 10.1093/hmg/dds425 23065704

29. Schumacher FR, Olama AAA, Berndt SI, Benlloch S, Ahmed M, Saunders EJ, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 2018; 50: 928–936. doi: 10.1038/s41588-018-0142-8 29892016

30. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 2015; 16: 197–212. doi: 10.1038/nrg3891 25707927

31. Dixon AL, Liang L, Moffatt MF, Chen W, Heath S, Wong KC, et al. A genome-wide association study of global gene expression. Nat. Genet. 2007; 39: 1202–1207. doi: 10.1038/ng2109 17873877

32. Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG. Common genetic variants account for differences in gene expression among ethnic groups. Nat. Genet. 2007; 39: 226–231. doi: 10.1038/ng1955 17206142

33. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al. Population genomics of human gene expression. Nat. Genet. 2007; 39: 1217–1224. doi: 10.1038/ng2142 17873874

34. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013; 45: 580–585. doi: 10.1038/ng.2653 23715323

35. GTEx Consortium. Genetic effects on gene expression across human tissues. Nature. 2017; 550: 204–213. doi: 10.1038/nature24277 29022597

36. Thibodeau SN, French AJ, McDonnell SK, Cheville J, Middha S, Tillmans L, et al. Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set. Nat. Commun. 2015; 6: 8653. doi: 10.1038/ncomms9653 26611117

37. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, Cox NJ. Trait-associated SNPs are more likely to be eQTLs: annotations to enhance discovery from GWAS. PLoS Genetics. 2010; 6: e1000888. doi: 10.1371/journal.pgen.1000888

38. Grisanzio C, Werner L, Takeda D, Awoyemi BC, Pomerantz MM, Yamada H, et al. Genetic and functional analyses implicate the NUDT11, HNF1B and SLC22A3 genes in prostate cancer pathogenesis. PNAS. 2012; 109: 11252–11257. doi: 10.1073/pnas.1200853109

39. Penney KL, Sinnott JA, Tyekucheva S, Gerke T, Shui IM, Kraft P, et al. Association of prostate cancer risk variants with gene expression in normal and tumor tissue. Cancer Epidemiol Biomarkers Prev. 2015; 24: 255–260. doi: 10.1158/1055-9965.EPI-14-0694-T 25371445

40. Wagner JR, Busche S, Ge B, Kwan T, Pastinen T, Blanchette M. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. Genome Biol. 2014; 15: R37. doi: 10.1186/gb-2014-15-2-r37 24555846

41. Banovich NE, Lan X, McVicker G, van de Geijn B, Degner JF, Blischak JD, et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 2014; 10: e1004663. doi: 10.1371/journal.pgen.1004663 25233095

42. Lemire M, Zaidi SH, Ban M, Ge B, Aïssi D, Germain M, et al. Long-range epigenetic regulation is conferred by genetic variation located at thousands of independent loci. Nat. Commun. 2015; 6: 6326. doi: 10.1038/ncomms7326 25716334

43. Portela A, Esteller M. Epigenetic modifications and human disease. Nat. Biotechnol. 2010; 28: 1057–1068. doi: 10.1038/nbt.1685 20944598

44. Massie CE, Mills IG, Lynch AG. The importance of DNA methylation in prostate cancer development Identification. Journal of Steriod Biochemistry and Molecular Biology. 2017; 166: 1–15.

45. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010; 6: e1000952. doi: 10.1371/journal.pgen.1000952 20485568

46. van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics. 2012; 13: 636. doi: 10.1186/1471-2164-13-636 23157493

47. Smith AK, Kilaru V, Kocak M, Almli LM, Mercer KB, Ressler KJ, et al. Methylation quantitative trait loci (meQTLs) are consistently detected across ancestry, developmental stage, and tissue type. BMC Genomics. 2014; 15: 145. doi: 10.1186/1471-2164-15-145 24555763

48. Drong AW, Nicholson G, Hedman AK, Meduri E, Grundberg E, Small KS, et al. The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue. PLoS One. 2013; 8: e55923. doi: 10.1371/journal.pone.0055923 23431366

49. Quon G, Lippert C, Heckerman D, Listgarten J. Patterns of methylation heritability in a genome-wide analysis of four brain regions. Nucleic Acids Res. 2013; 41: 2095–2104. doi: 10.1093/nar/gks1449 23303775

50. Banovich NE, Lan X, McVicker G, van de Geijn B, Degner JF, Blischak JD, et al. Methylation QTLs are associated with coordinated changes in transcription factor binding, histone modifications, and gene expression levels. PLoS Genet. 2014; 10: e1004663. doi: 10.1371/journal.pgen.1004663 25233095

51. Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. eLife. 2013; 2: e00523. doi: 10.7554/eLife.00523 23755361

52. van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, et al. Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects. BMC Genomics. 2012; 13: 636. doi: 10.1186/1471-2164-13-636 23157493

53. Pierce BL, Tong L, Argos M, Demanelis K, Jasmine F, Rakibuz-Zaman M, et al. Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms. Nat. Commun. 2018; 9: 804. doi: 10.1038/s41467-018-03209-9 29476079

54. Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genetics. 2017; 13: e1007081. doi: 10.1371/journal.pgen.1007081 29149188

55. Millstein J, Zhang B, Zhu J, Schadt EE. Disentangling molecular relationships with a causal inference test. BMC Genet. 2009; 10: 23. doi: 10.1186/1471-2156-10-23 19473544

56. Troyer DA, Lucia MS, de Bruïne AP, Mendez-Meza R, Baldewijns MM, Dunscomb N, et al. Prostate Cancer Detected by Methylated Gene Markers in Histopathologically cancer-negative tissues from men with subsequent positive biopsies. Cancer Epidemiol Biomarkers Prev. 2009; 18(10): 2717–2722. doi: 10.1158/1055-9965.EPI-09-0068 19755651

57. Kosari F, Cheville JC, Ida CM, Karnes RJ, Leontovich AA, Sebo TJ, et al. Shared gene expression alterations in prostate cancer and histologically benign prostate from patients with prostate cancer. American Journal of Pathology. 2012; 181(1): 34–42. doi: 10.1016/j.ajpath.2012.03.043 22640805

58. Moller M, Strand SH, Mundbjerg K, Liang G, Gill I, Haldrup C, et al. Heterogeneous patterns of DNA methylation-based field effects in histologically normal prostate tissue from cancer patients. Scientific Reports. 2017; 7: 40636. doi: 10.1038/srep40636 28084441

59. Nguyen HH, Takata R, Akamatsu S, Shigemizu D, Tsunoda T, Furihata M, et al. IRX4 at 5p15 suppresses prostate cancer growth through interaction with vitamin D receptor, conferring prostate cancer susceptibility. Hum Mol Genet. 2012; 21: 2076–2085. doi: 10.1093/hmg/dds025

60. Xu X, Hussain WM, Vijai J, Offit K, Rubin MA, Demichelis F, et al. Variants at IRX4 as prostate cancer expression quantitative trait loci. Eur J Hum Genet. 2014; 22: 558–563. doi: 10.1038/ejhg.2013.195 24022300

61. Ross-Adams H, Ball S, Lawrenson K, Halim S, Russell R, Wells C, et al. HNF1B variants associate with promoter methylation and regulate gene networks activated in prostate and ovarian cancer. Oncotarget. 2016; 7: 74734–74746. doi: 10.18632/oncotarget.12543 27732966

62. Hu YL, Zhong D, Pang F, Ning QY, Zhang YY, Li G, et al. HNF1B is involved in prostate cancer risk via modulating androgenic hormone effects and coordination with other genes. Genet Mol Res. 2013; 12: 1327–1335. doi: 10.4238/2013.April.25.4 23661456

63. Liao D. Emerging role of the EBF family of transcription factors in tumor suppression. Mol Cancer Res. 2009; 7: 1893–1901. doi: 10.1158/1541-7786.MCR-09-0229

64. Amin Al Olama A, Dadaev T, Hazelett DJ, Li Q, Leongamornlert D, Saunders EJ, et al. Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans. Hum Mol Genet. 2015; 24(19): 5589–5602. doi: 10.1093/hmg/ddv203 26025378

65. Li Q, Stram A, Chen C, Kar S, Gayther S, Pharoah P, et al. Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types. Human Molecular Genetics 2014; 23: 5294–5302. doi: 10.1093/hmg/ddu228 24907074

66. Nickerson ML, Das S, Im KM, Turan S, Berndt SI, Li H, et al. TET2 binds the androgen receptor and loss is associated with prostate cancer. Oncogene 2017; 36(15): 2172–2183. doi: 10.1038/onc.2016.376 27819678

67. Patra SK, Petra A, Zhao H, Dahiya R. DNA methyltransferase and demethylase in human prostate cancer. Mol Carcinog. 2002; 33(3): 163–171. doi: 10.1002/mc.10033 11870882

68. Seetharaman S, Flemyng E, Shen J, Conte MR, Ridley AJ. The RNA-binding protein LARP4 regulates cancer cell migration and invasion. Cytoskeleton (Hoboken). 2006; 73(11): 680–690.

69. Bu H, Narisu N, Schlick B, Rainer J, Manke T, Schäfer G, et al. Putative prostate cancer risk SNP in an androgen receptor-binding site of the melanophilin gene illustrates enrichment of risk sNPs in androgen receptor target sites. Hum. Mutat. 2016; 37(1): 52–64. doi: 10.1002/humu.22909 26411452

70. Bodle CR, Mackie DI, Roman DL. RGS17: an emerging therapeutic target for lung and prostate cancers. Future Med Chem. 2013; 5(9): 995–1007. doi: 10.4155/fmc.13.91 23734683

71. James MA, Lu Y, Liu Y, Vikis HG, You M. RGS17, an overexpressed gene in human lung and prostate cancer, induces tumor cell proliferation through the cyclic AMP-PKA-CREB pathway. Cancer Research. 2009; 69(5): 2018–2016. doi: 10.1158/0008-5472.CAN-08-3589

72. Pomerantz MM, Shrestha Y, Flavin RJ, Regan MM, Penney KL, Mucci LA, et al. Analysis of the 10q11 cancer risk locus implicates MSMB and NCOA4 in human prostate tumorigenesis. PLoS genetics 2010; 6: e1001204. doi: 10.1371/journal.pgen.1001204 21085629

73. Whitaker HC, Kote-Jarai Z, Ross-Adams H, Warren AY, Burge J, George A, et al. The rs10993994 risk allele for prostate cancer results in clinically relevant changes in microseminoprotein-beta expression in tissue and urine. PloS One 2010; 5: e13363. doi: 10.1371/journal.pone.0013363 20967219

74. Han Y, Hazelett DJ, Wiklund F, Schumacher FR, Stram DO, Berndt SI, et al. Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions. Hum Mol Genet. 2015; 24(19): 5603–5618. doi: 10.1093/hmg/ddv269 26162851

75. He XH, Li JJ, Xie YH, Tang YT, Yao GF, Qin WX, et al. Altered gene expression profiles of NIH3T3 cells regulated by human lung cancer associated gene CT120. Cell Res. 2004. 14(6): 487–496. doi: 10.1038/sj.cr.7290252 15625016

76. Zhang J, Kuang Y, Wang Y, Xu Q, Ren Q. Notch-4 silencing inhibits prostate cancer growth and EMT via the NF-κB pathway. Apoptosis 2017; 22(6): 877–884. doi: 10.1007/s10495-017-1368-0

77. Ongen H, Andersen CL, Bramsen JB, Oster B, Rasmussen MH, Ferreira PG, et al. Putative cis-regulatory drivers in colorectal cancer. Nature. 2014; 512: 87–90. doi: 10.1038/nature13602 25079323

78. Drake CG. Prostate cancer as a model for tumour immunotherapy. Nat. Rev. Immunol. 2010; 10: 580–593. doi: 10.1038/nri2817 20651745

79. Wang X, Yu J, Sreekumar A, Varambally S, Shen R, Giacherio D, et al. Antibody signatures in prostate cancer. N. Engl. J. Med. 2005; 353: 1224–1235. doi: 10.1056/NEJMoa051931 16177248

80. Noguchi M, Koga N, Moriya F, Itoh K. Immunotherapy in prostate cancer: challenges and opportunities. Immunotherapy 2016; 8: 69–77. doi: 10.2217/imt.15.101 26642100

81. Agalliu I, Salinas CA, Hansten PD, Ostrander EA, Stanford JL. Statin use and risk of prostate cancer: results from a population-based epidemiologic study. Am J Epidemiol. 2008; 168: 250–260. doi: 10.1093/aje/kwn141 18556686

82. Stanford JL, Wicklund KG, McKnight B, Daling JR, Brawer MK. Vasectomy and risk of prostate cancer. Cancer Epidemiol Biomarkers Prev. 1999; 8: 881–886. 10548316

83. Zhao S, Geybels MS, Leonardson A, Rubicz R, Kolb S, Yan Q, et al. Epigenome-wide tumor DNA methylation profiling identifies novel prognostic biomarkers of metastatic-lethal progression in men diagnosed with clinically localized prostate cancer. Clinical Cancer Research. 2017; 23: 311–319. doi: 10.1158/1078-0432.CCR-16-0549 27358489

84. The Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell. 2015; 163: 1011–1025. doi: 10.1016/j.cell.2015.10.025 26544944

85. Delaneau O, Marchini J, Zagury J. A linear complexity phasing method for thousands of genomes. Nature Methods. 2011; 9: 179–181. doi: 10.1038/nmeth.1785 22138821

86. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics. 2009; 5: e1000529. doi: 10.1371/journal.pgen.1000529 19543373

87. Bolstad B. preprocessCore: A collection of pre-processing functions. R package version 1.44.0, https://github.com/bmbolstad/preprocessCore. 2018.

88. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014; 30(10): 1363–1369. doi: 10.1093/bioinformatics/btu049 24478339

89. Chen Y, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013; 8(2): 203–209. doi: 10.4161/epi.23470 23314698

90. Hannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, et al. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci, Nature Neuroscience. 2016; 19(1): 48–54. doi: 10.1038/nn.4182 26619357

91. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007; 8: 118–127. doi: 10.1093/biostatistics/kxj037 16632515

92. Maksimovic J, Gordon L, Oshlack A. SWAN: Subset-quantile within array normalization for Illumina Infinium HumanMethylation450 BeadChips. Genome Biology. 2012; 13: R44. doi: 10.1186/gb-2012-13-6-r44 22703947

93. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 2006; 38: 904–909. doi: 10.1038/ng1847 16862161

94. Stegle O, Parts L, Durbin R, Winn J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Computational Biology. 2010; 6: e1000770. doi: 10.1371/journal.pcbi.1000770 20463871

95. Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips, Bioinformatics. 2017; 33(24): 3982–3984. doi: 10.1093/bioinformatics/btx513 28961746

96. Storey JD, Bass AJ, Dabney A, Robinson D, Warnes G. qvalue: Q-value estimation for false discovery rate control. R package version 2.14.1, http://github.com/jdstorey/qvalue. 2019.

97. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139–140. doi: 10.1093/bioinformatics/btp616 19910308

98. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015; 43(7): e47. doi: 10.1093/nar/gkv007 25605792

99. Højsgaard S, Halekoh U, Yan J. The R package geepack for generalized estimating equations. Journal of Statistical Software. 2006; 15(2): 1–11.

Článek Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

Článek Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering

Článek Drosophila insulin-like peptide 2 mediates dietary regulation of sleep intensity

Článek The alarmones (p)ppGpp are part of the heat shock response of Bacillus subtilis

Článek RNA Polymerase II CTD phosphatase Rtr1 fine-tunes transcription termination

Článek Modeling cancer genomic data in yeast reveals selection against ATM function during tumorigenesis

Článek The Caenorhabditis elegans homolog of the Evi1 proto-oncogene, egl-43, coordinates G1 cell cycle arrest with pro-invasive gene expression during anchor cell invasion

Článek Transcription-replication conflicts as a source of common fragile site instability caused by BMI1-RNF2 deficiency

Článek The Lid/KDM5 histone demethylase complex activates a critical effector of the oocyte-to-zygote transition

Článek Tracking human population structure through time from whole genome sequences

Článek FLS2 is a CDK-like kinase that directly binds IFT70 and is required for proper ciliary disassembly in Chlamydomonas