Imputation of the Rare G84E Mutation and Cancer Risk in a Large Population-Based Cohort

English version České info

An efficient approach to characterizing the disease burden of rare genetic variants is to impute them into existing well-phenotyped cohorts with genome-wide data by using large sequenced reference panels; however, the efficacy of this approach remains controversial. A recent study suggested that it is not possible to impute the rare HOXB13 G84E variant using neighboring SNP markers. We show that by using an enriched reference sequenced sample of 22 mutation carriers, we were able to impute this mutation into a large cohort of 83,285 non-Hispanic White individuals from the Kaiser Permanente Research Program on Genes, Environment, and Health Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. The imputation was confirmed via a novel classification and regression tree method, and then empirically validated by direct mutation genotyping of a subset of 1,673 of these individuals in addition to 1,789 other men from Kaiser. Using the same GERA cohort, we then confirmed that the G84E mutation is associated with increased risk of prostate cancer, and estimated the age-specific risk for carriers of the mutation. Finally, we obtained evidence that the mutation is associated with additional types of cancer in the GERA cohort.

Vyšlo v časopise: Imputation of the Rare G84E Mutation and Cancer Risk in a Large Population-Based Cohort. PLoS Genet 11(1): e32767. doi:10.1371/journal.pgen.1004930
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004930

Souhrn

Zdroje

1. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB (2010) Rare Variants Create Synthetic Genome-Wide Associations. PLoS Biol 8: e1000294. 20126254

2. Sham PC, Purcell SM (2014) Statistical power and significance testing in large-scale genetic studies. Nat Rev Genet 15: 335–346. 24739678

3. Cirulli ET, Goldstein DB (2010) Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 11: 415–425. doi: 10.1038/nrg2779 20479773

4. Welter D, MacArthur J, Morales J, Burdett T, Hall P, et al. (2014) The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42: D1001–D1006. doi: 10.1093/nar/gkt1229 24316577

5. Zheng H-F, Ladouceur M, Greenwood CMT, Richards JB (2012) Effect of Genome-Wide Genotyping and Reference Panels on Rare Variants Imputation. Journal of Genetics and Genomics 39: 545–550. 23089364

6. Auer PL, Johnsen JM, Johnson AD, Logsdon BA, Lange LA, et al. (2012) Imputation of Exome Sequence Variants into Population- Based Samples and Blood-Cell-Trait-Associated Loci in African Americans: NHLBI GO Exome Sequencing Project. The American Journal of Human Genetics 91: 794–808. doi: 10.1016/j.ajhg.2012.08.031 23103231

7. Duan Q, Liu EY, Auer PL, Zhang G, Lange EM, et al. (2013) Imputation of coding variants in African Americans: better performance using data from the exome sequencing project. Bioinformatics 29: 2744–2749. doi: 10.1093/bioinformatics/btt477 23956302

8. Holm H, Gudbjartsson DF, Sulem P, Masson G, Helgadottir HT, et al. (2011) A rare variant in MYH6 is associated with high risk of sick sinus syndrome. Nature Genetics 43: 316–320. doi: 10.1038/ng.781 21378987

9. Jewett EM, Zawistowski M, Rosenberg NA, Zöllner S (2012) A Coalescent Model for Genotype Imputation. Genetics 191: 1239–1255. 22595242

10. Joshi PK, Prendergast J, Fraser RM, Huffman JE, Vitart V, et al. (2013) Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies. PLoS ONE 8: e68604. doi: 10.1371/journal.pone.0068604 23874685

11. Jostins L, Morley KI, Barrett JC (2011) Imputation of low-frequency variants using the HapMap3 benefits from large, diverse reference sets. European Journal of Human Genetics 19: 662–666. doi: 10.1038/ejhg.2011.10 21364697

12. Li L, Li Y, Browning SR, Browning BL, Slater AJ, et al. (2011) Performance of Genotype Imputation for Rare Variants Identified in Exons and Flanking Regions of Genes. PLoS ONE 6: e24945. doi: 10.1371/journal.pone.0024945 21949800

13. Liu EY, Buyske S, Aragaki AK, Peters U, Boerwinkle E, et al. (2012) Genotype Imputation of MetabochipSNPs Using a Study-Specific Reference Panel of ∼4,000 Haplotypes in African Americans From the Women’s Health Initiative. Genetic Epidemiology 36: 107–117. doi: 10.1002/gepi.21603 22851474

14. Mägi R, Asimit JL, Day-Williams AG, Zeggini E, Morris AP (2012) Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases. Genetic Epidemiology 36: 785–796. doi: 10.1002/gepi.21675 22951892

15. Sung YJ, Wang L, Rankinen T, Bouchard C, Rao DC (2012) Performance of Genotype Imputations Using Data from the 1000 Genomes Project. Human Heredity 73: 18–25. 22212296

16. Wood AR, Perry JRB, Tanaka T, Hernandez DG, Zheng H-F, et al. (2013) Imputation of Variants from the 1000 Genomes Project Modestly Improves Known Associations and Can Identify Low-frequency Variant—Phenotype Associations Undetected by HapMap Based Imputation. PLoS ONE 8: e64343. doi: 10.1371/journal.pone.0064343 23696881

17. Akbari MR, Trachtenberg J, Lee J, Tam S, Bristow R, et al. (2012) Association Between Germline HOXB13 G84E Mutation and Risk of Prostate Cancer. JNCI J Natl Cancer Inst 104: 1260–1262. 22781434

18. Breyer JP, Avritt TG, McReynolds KM, Dupont WD, Smith JR (2012) Confirmation of the HOXB13 G84E Germline Mutation in Familial Prostate Cancer. Cancer Epidemiol Biomarkers Prev 21: 1348–1353. 22714738

19. Chen Z, Greenwood C, Isaacs WB, Foulkes WD, Sun J, et al. (2013) The G84E mutation of HOXB13 is associated with increased risk for prostate cancer: results from the REDUCE trial. Carcinogenesis 34: 1260–1264. doi: 10.1093/carcin/bgt055 23393222

20. Ewing CM, Ray AM, Lange EM, Zuhlke KA, Robbins CM, et al. (2012) Germline mutations in HOXB13 and prostate-cancer risk. New England Journal of Medicine 366: 141–149. 22236224

21. Gudmundsson J, Sulem P, Gudbjartsson DF, Masson G, Agnarsson BA, et al. (2012) A study based on whole-genome sequencing yields a rare variant at 8q24 associated with prostate cancer. Nature Genetics 44: 1326–9. doi: 10.1038/ng.2437 23104005

22. Huang H, Cai B (2014) G84E mutation in HOXB13 is firmly associated with prostate cancer risk: a meta-analysis. Tumor Biol 35: 1177–1182. doi: 10.1007/s13277-013-1157-5 24026887

23. International Consortium for Prostate Cancer Genetics, Xu J, Lange EM, Lu L, Zheng SL, et al. (2012) HOXB13 is a susceptibility gene for prostate cancer: results from the International Consortium for Prostate Cancer Genetics (ICPCG). Human Genetics.

24. Karlsson R, Aly M, Clements M, Zheng L, Adolfsson J, et al. (2014) A Population-based Assessment of Germline HOXB13 G84E Mutation and Prostate Cancer Risk. European Urology 65: 169–176. 22841674

25. Kluźniak W, Wokołorczyk D, Kashyap A, Jakubowska A, Gronwald J, et al. (2013) The G84E mutation in the HOXB13 gene is associated with an increased risk of prostate cancer in Poland. The Prostate 73: 542–548. doi: 10.1002/pros.22594 23334858

26. Laitinen VH, Wahlfors T, Saaristo L, Rantapero T, Pelttari LM, et al. (2013) HOXB13 G84E Mutation in Finland: Population-Based Analysis of Prostate, Breast, and Colorectal Cancer Risk. Cancer Epidemiol Biomarkers Prev 22: 452–460. doi: 10.1158/1055-9965.EPI-12-1000-T 23292082

27. MacInnis RJ, Severi G, Baglietto L, Dowty JG, Jenkins MA, et al. (2013) Population-Based Estimate of Prostate Cancer Risk for Carriers of the HOXB13 Missense Mutation G84E. PLoS ONE 8: e54727. doi: 10.1371/journal.pone.0054727 23457453

28. Shang Z, Zhu S, Zhang H, Li L, Niu Y (2013) Germline Homeobox B13 (HOXB13) G84E Mutation and Prostate Cancer Risk in European Descendants: A Meta-analysis of 24 213 Cases and 73 631 Controls. European Urology 64: 173–176. doi: 10.1016/j.eururo.2013.03.007 23518396

29. Stott-Miller M, Karyadi DM, Smith T, Kwon EM, Kolb S, et al. (2013) HOXB13 mutations in a population-based, case-control study of prostate cancer. The Prostate 73: 634–641. doi: 10.1002/pros.22604 23129385

30. Witte JS, Mefford J, Plummer SJ, Liu J, Cheng I, et al. (2013) HOXB13 Mutation and Prostate Cancer: Studies of Siblings and Aggressive Disease. Cancer Epidemiol Biomarkers Prev 22: 675–680. doi: 10.1158/1055-9965.EPI-12-1154 23396964

31. Miao J, Wang Z, Provencher H, Muir B, Dahiya S, et al. (2007) HOXB13 promotes ovarian cancer progression. PNAS 104: 17093–17098. 17942676

32. Marra L, Cantile M, Scognamiglio G, Perdonà S, La Mantia E, et al. (2013) Deregulation of HOX B13 expression in urinary bladder cancer progression. Curr Med Chem 20: 833–839. 23276138

33. Sabatino R, Cantile M, Aquino G, Scognamiglio G, Madonna C, et al. (2013) PP076: HOX B13 and HOX C13 expression in oral squamous cell carcinoma: A tissue microarray based immunohistochemical study. Oral Oncology 49, Supplement 1: S120.

34. Jerevall P-L, Jansson A, Fornander T, Skoog L, Nordenskjöld B, et al. (2010) Predictive relevance of HOXB13 protein expression for tamoxifen benefit in breast cancer. Breast Cancer Res 12: R53. doi: 10.1186/bcr2612 20649975

35. Akbari MR, Anderson LN, Buchanan DD, Clendenning M, Jenkins MA, et al. (2013) Germline HOXB13 p.Gly84Glu mutation and risk of colorectal cancer. Cancer Epidemiology 37: 424–427. doi: 10.1016/j.canep.2013.03.003 23541221

36. Alanee S, Couch F, Offit K (2012) Association of a HOXB13 Variant with Breast Cancer. New England Journal of Medicine 367: 480–481. doi: 10.1056/NEJMc1205138 22853031

37. Saunders EJ, Dadaev T, Leongamornlert DA, Jugurnauth-Little S, Tymrakiewicz M, et al. (2014) Fine-Mapping the HOXB Region Detects Common Variants Tagging a Rare Coding Allele: Evidence for Synthetic Association in Prostate Cancer. PLoS Genet 10: e1004129. doi: 10.1371/journal.pgen.1004129 24550738

38. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, et al. (2007) PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics 81: 559–575. 17701901

39. Enger SM, Van Den Eeden SK, Sternfeld B, Loo RK, Quesenberry CP, et al. (2006) California Men’s Health Study (CMHS): a multiethnic cohort in a managed care setting. BMC Public Health 6: 172. 16813653

40. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group. 376 p.

41. Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, et al. (2008) Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation. Science 319: 1100–1104. doi: 10.1126/science.1153717 18292342

42. Hoffmann TJ, Kvale MN, Hesselson SE, Zhan Y, Aquino C, et al. (2011) Next generation genome-wide association tool: Design and coverage of a high-throughput European-optimized SNP array. Genomics 98: 79–89. doi: 10.1016/j.ygeno.2011.04.005 21565264

43. Jung C, Kim R-S, Zhang H-J, Lee S-J, Jeng M-H (2004) HOXB13 Induces Growth Suppression of Prostate Cancer Cells as a Repressor of Hormone-Activated Androgen Receptor Signaling. Cancer Res 64: 9185–9192. 15604291

44. Norris JD, Chang C-Y, Wittmann BM, Kunder RS, Cui H, et al. (2009) The Homeodomain Protein HOXB13 Regulates the Cellular Response to Androgens. Molecular Cell 36: 405–416. doi: 10.1016/j.molcel.2009.10.020 19917249

45. Howie BN, Donnelly P, Marchini J (2009) A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies. PLoS Genet 5: e1000529. doi: 10.1371/journal.pgen.1000529 19543373

46. Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19: 1655–1664. doi: 10.1101/gr.094052.109 19648217

47. Hoffmann TJ, Zhan Y, Kvale MN, Hesselson SE, Gollub J, et al. (2011) Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm. Genomics 98: 422–430. doi: 10.1016/j.ygeno.2011.08.007 21903159

48. Delaneau O, Marchini J, Zagury J-F (2012) A linear complexity phasing method for thousands of genomes. Nature Methods 9: 179–181. doi: 10.1038/nmeth.1785 22138821

49. Howie B, Marchini J, Stephens M (2011) Genotype Imputation with Thousands of Genomes. G3 1: 457–470. 22384356

50. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nature Genetics 44: 955–959. doi: 10.1038/ng.2354 22820512

51. Marchini J, Howie B (2010) Genotype imputation for genome-wide association studies. Nat Rev Genet 11: 499–511. 20517342

52. R Core Team (2012) R: A language and environment for statistical computing. Available: http://www.R-project.org.

53. Efron B (1987) Better Bootstrap Confidence Intervals. Journal of the American Statistical Association 82: 171–185.

54. Huang L, Li Y, Singleton AB, Hardy JA, Abecasis G, et al. (2009) Genotype-Imputation Accuracy across Worldwide Human Populations. The American Journal of Human Genetics 84: 235–250. doi: 10.1016/j.ajhg.2009.01.013 19215730

55. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics 38: 904–909. 16862161

56. Bhattacharjee S, Rajaraman P, Jacobs KB, Wheeler WA, Melin BS, et al. (2012) A Subset-Based Approach Improves Power and Interpretation for the Combined Analysis of Genetic Association Studies of Heterogeneous Traits. The American Journal of Human Genetics 90: 821–835. doi: 10.1016/j.ajhg.2012.03.015 22560090