Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the breast and prostate cancer cohort consortium

American Journal of Epidemiology

Volumn 180, Issue 10, 2014, Pages 1018-1027

  Joshi, Amit D ^a   Lindström, Sara ^a   Hüsing, Anika ^b   Barrdahl, Myrto ^b   Van Der Weele, Tyler J ^a   Campa, Daniele ^b,c   Canzian, Federico ^b   Gaudet, Mia M ^d   Figueroa, Jonine D ^e   Baglietto, Laura ^f,g   Berg, Christine D ^e   Buring, Julie E ^h   Chanock, Stephen J ^e   Chirlaque, María Dolores ^i,j   Ryan Diver, W ^d   Dossus, Laure ^k,l   Giles, Graham G ^f,g,m   Haiman, Christopher A ⁿ   Hankinson, Susan E ^o   Henderson, Brian E ⁿ   Hoover, Robert N ^e   Hunter, David J ^a   Isaacs, Claudine ^p   Kaaks, Rudolf ^b   Kolonel, Laurence N ^q   Krogh, Vittorio ^r   Marchand, Loic Le ^q   Lee, I Min ^h   Lund, Eiliv ^s   McCarty, Catherine A ^t   Overvad, Kim ^u   Peeters, Petra H ^v,w   Riboli, Elio ^w   Schumacher, Fredrick ⁿ   Severi, Gianluca ^f,g   Stram, Daniel O ⁿ   Sund, Malin ^x   Thun, Michael J ^d   Travis, Ruth C ^y   Trichopoulos, Dimitrios ^a,z,aa   Willett, Walter C ^a   Zhang, Shumin ^h   Ziegler, Regina G ^e   Kraft, Peter ^a   more..

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^c UNIVERSITY OF PISA   (Italy)

^d AMERICAN CANCER SOCIETY   (United States)

^e NATIONAL CANCER INSTITUTE   (United States)

^f CANCER EPIDEMIOLOGY CENTRE   (Australia)

^g UNIVERSITY OF MELBOURNE   (Australia)

^h HARVARD MEDICAL SCHOOL   (United States)

ⁱ DEPARTMENT OF EPIDEMIOLOGY   (Spain)

^j Research Institute i 12   (Spain)

^k INSERM   (France)

^l UNIVERSITÉ PARIS SACLAY   (France)

^m MONASH UNIVERSITY   (Australia)

ⁿ UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^o UNIVERSITY OF MASSACHUSETTS   (United States)

^p GEORGETOWN UNIVERSITY   (United States)

^q UNIVERSITY OF HAWAII CANCER CENTER   (United States)

^r NATIONAL CANCER INSTITUTE   (Italy)

^s UNIVERSITY OF TROMSØ   (Norway)

^t ESSENTIA INSTITUTE OF RURAL HEALTH   (United States)

^u AARHUS UNIVERSITY   (Denmark)

^v UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^w IMPERIAL COLLEGE LONDON   (United Kingdom)

^x UMEÅ UNIVERSITY HOSPITAL   (Sweden)

^y UNIVERSITY OF OXFORD   (United Kingdom)

^z HELLENIC HEALTH FOUNDATION   (Greece)

^aa BIOMEDICAL RESEARCH FOUNDATION   (Greece)

more..

Author keywords

Additive interactions; Breast cancer; Genome wide association studies; Single nucleotide polymorphisms

Indexed keywords

ETIOLOGY; GENOME; NUMERICAL MODEL; POLYMORPHISM; PREDICTION; RISK FACTOR; WOMENS HEALTH;

ADOLESCENT; ALLELE; ARTICLE; BODY MASS; BREAST CANCER; CANCER RISK; CAUCASIAN; COHORT ANALYSIS; CONTROLLED STUDY; FEMALE; GENE; GENE INTERACTION; GENETIC ASSOCIATION; GENOTYPE; HUMAN; MAJOR CLINICAL STUDY; MODEL; NATIONAL HEALTH ORGANIZATION; PREDICTION; PROSTATE CANCER; RAD51 PROTEIN HOMOLOG 2 GENE; RISK ASSESSMENT; SINGLE NUCLEOTIDE POLYMORPHISM; AUSTRALIA; BLOOD; BREAST NEOPLASMS; CASE CONTROL STUDY; CLINICAL TRIAL; EUROPE; GENETIC PREDISPOSITION; GENETICS; MALE; MIDDLE AGED; MULTICENTER STUDY; ODDS RATIO; PROSTATIC NEOPLASMS; RISK FACTOR; UNITED STATES;

DNA BINDING PROTEIN; RAD51 PROTEIN; RAD51 PROTEIN, HUMAN; RAD51B PROTEIN, HUMAN; TUMOR MARKER;

ALLELES; AUSTRALIA; BIOMARKERS, TUMOR; BODY MASS INDEX; BREAST NEOPLASMS; CASE-CONTROL STUDIES; COHORT STUDIES; DNA-BINDING PROTEINS; EUROPE; EUROPEAN CONTINENTAL ANCESTRY GROUP; FEMALE; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; MALE; MIDDLE AGED; ODDS RATIO; POLYMORPHISM, SINGLE NUCLEOTIDE; PROSTATIC NEOPLASMS; RAD51 RECOMBINASE; RISK ASSESSMENT; RISK FACTORS; UNITED STATES;

EID: 84911419142     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwu214     Document Type: Article

References (66)

1. Easton DF, Pooley KA, Dunning AM, et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature. 2007;447(7148):1087-1093.
2. Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2007;39(7):865-869.
3. Stacey SN, Manolescu A, Sulem P, et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet. 2008;40(6): 703-706.
4. Thomas G, Jacobs KB, Kraft P, et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet. 2009;41(5):579-584.
5. Turnbull C, Ahmed S, Morrison J, et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet. 2010;42(6):504-507.
6. Fletcher O, Johnson N, Orr N, et al. Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study. J Natl Cancer Inst. 2011;103(5):425-435.
7. Hunter DJ, Kraft P, Jacobs KB, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. 2007;39(7):870-874.
8. Cox A, Dunning AM, Garcia-Closas M, et al. A common coding variant in CASP8 is associated with breast cancer risk. Nat Genet. 2007;39(3):352-358.
9. Gold B, Kirchhoff T, Stefanov S, et al. Genome-wide association study provides evidence for a breast cancer risk locus at 6q22.33. Proc Natl Acad Sci USA. 2008;105(11): 4340-4345.
10. Zheng W, Long J, Gao YT, et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat Genet. 2009;41(3):324-328.
11. Ahmed S, Thomas G, Ghoussaini M, et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet. 2009;41(5):585-590.
12. Michailidou K, Hall P, Gonzalez-Neira A, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353-361, 361e351-361e352.
13. Antoniou AC, Wang X, Fredericksen ZS, et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet. 2010;42(10): 885-892.
14. Haiman CA, Chen GK, Vachon CM, et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat Genet. 2011;43(12): 1210-1214.
15. Siddiq A, Couch FJ, Chen GK, et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum Mol Genet. 2012;21(24):5373-5384.
16. Garcia-Closas M, Couch FJ, Lindstrom S, et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet. 2013;45(4):392-398, 398e391-398e392.
17. Travis RC, Reeves GK, Green J, et al. Gene-environment interactions in 7610 women with breast cancer: prospective evidence from the Million Women Study. Lancet. 2010; 375(9732):2143-2151.
18. Milne RL, Gaudet MM, Spurdle AB, et al. Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the Breast Cancer Association Consortium: a combined case-control study. Breast Cancer Res. 2010;12(6):R110.
19. Campa D, Kaaks R, Le Marchand L, et al. Interactions between genetic variants and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium. J Natl Cancer Inst. 2011; 103(16):1252-1263.
20. Nickels S, Truong T, Hein R, et al. Evidence of gene-environment interactions between common breast cancer susceptibility loci and established environmental risk factors. PLoS Genet. 2013;9(3):e1003284.
21. Knol MJ, Egger M, Scott P, et al. When one depends on the other: reporting of interaction in case-control and cohort studies. Epidemiology. 2009;20(2):161-166.
22. Pharoah PD, Antoniou AC, Easton DF, et al. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008;358(26):2796-2803.
23. Gail MH. Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst. 2009;101(13):959-963.
24. Mealiffe ME, Stokowski RP, Rhees BK, et al. Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information. J Natl Cancer Inst. 2010;102(21): 1618-1627.
25. Rothman KJ. Synergy and antagonism in cause-effect relationships. Am J Epidemiol. 1974;99(6):385-388.
26. Siemiatycki J, Thomas DC. Biological models and statistical interactions: an example from multistage carcinogenesis. Int J Epidemiol. 1981;10(4):383-387.
27. Garcia-Closas M, Rothman N, Figueroa JD, et al. Common genetic polymorphisms modify the effect of smoking on absolute risk of bladder cancer. Cancer Res. 2013;73(7): 2211-2220.
28. Han SS, Rosenberg PS, Garcia-Closas M, et al. Likelihood ratio test for detecting gene (G)-environment (E) interactions under an additive risk model exploiting G-E independence for case-control data. Am J Epidemiol. 2012;176(11):1060-1067.
29. Hosmer DW, Lemeshow S. Confidence interval estimation of interaction. Epidemiology. 1992;3(5):452-456.
30. Nie L, Chu H, Li F, et al. Relative excess risk due to interaction: resampling-based confidence intervals. Epidemiology. 2010; 21(4):552-556.
31. Assmann SF, Hosmer DW, Lemeshow S, et al. Confidence intervals for measures of interaction. Epidemiology. 1996;7(3): 286-290.
32. VanderWeele TJ. A word and that to which it once referred: assessing "biologic" interaction. Epidemiology. 2011;22(4): 612-613.
33. VanderWeele TJ, Robins JM. The identification of synergism in the sufficient-component-cause framework. Epidemiology. 2007;18(3):329-339.
34. Gail MH. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst. 2008;100(14):1037-1041.
35. Darabi H, Czene K, Zhao W, et al. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res. 2012;14(1):R25.
36. Pashayan N, Duffy SW, Chowdhury S, et al. Polygenic susceptibility to prostate and breast cancer: implications for personalised screening. Br J Cancer. 2011;104(10): 1656-1663.
37. Husing A, Canzian F, Beckmann L, et al. Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status. J Med Genet. 2012;49(9):601-608.
38. Moonesinghe R, Khoury MJ, Liu T, et al. Discriminative accuracy of genomic profiling comparing multiplicative and additive risk models. Eur J Hum Genet. 2011;19(2): 180-185.
39. Hunter DJ, Riboli E, Haiman CA, et al. A candidate gene approach to searching for low-penetrance breast and prostate cancer genes. Nat Rev Cancer. 2005;5(12):977-985.
40. Calle EE, Rodriguez C, Jacobs EJ, et al. The American Cancer Society Cancer Prevention Study II Nutrition Cohort: rationale, study design, and baseline characteristics. Cancer. 2002;94(9): 2490-2501.
41. Riboli E, Hunt KJ, Slimani N, et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr. 2002; 5(6B):1113-1124.
42. Kolonel LN, Altshuler D, Henderson BE. The Multiethnic Cohort Study: exploring genes, lifestyle and cancer risk. Nat Rev Cancer. 2004;4(7):519-527.
43. Colditz GA, Hankinson SE. The Nurses' Health Study: lifestyle and health among women. Nat Rev Cancer. 2005;5(5):388-396.
44. Hayes RB, Reding D, Kopp W, et al. Etiologic and early marker studies in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Control Clin Trials. 2000;21(6 suppl): 349S-355S.
45. Rexrode KM, Lee IM, Cook NR, et al. Baseline characteristics of participants in the Women's Health Study. J Womens Health Gend Based Med. 2000;9(1):19-27.
46. Giles GG, English DR. The Melbourne Collaborative Cohort Study. IARC Sci Publ. 2002;156:69-70.
47. VanderWeele TJ, Robins JM. Directed acyclic graphs, sufficient causes, and the properties of conditioning on a common effect. Am J Epidemiol. 2007;166(9):1096-1104.
48. Höfler M. Causal inference based on counterfactuals. BMC Med Res Methodol. 2005;5:28.
49. Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81(24): 1879-1886.
50. Dupont WD. Converting relative risks to absolute risks: a graphical approach. Stat Med. 1989;8(6):641-651.
51. Yang Q, Khoury MJ, Botto L, et al. Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes. Am J Hum Genet. 2003;72(3):636-649.
52. Howlader N, Noone AM, Krapcho M, et al. SEER Cancer Statistics Review (CSR) 1975-2011. Bethesda, MD: National Cancer Institute; 2014. http://seer.cancer.gov/csr/1975-2011/. Based on November 2013 SEER data submission. Published April 2014. Updated August 27, 2014. Accessed April 14, 2014.
53. Knol MJ, VanderWeele TJ, Groenwold RH, et al. Estimating measures of interaction on an additive scale for preventive exposures. Eur J Epidemiol. 2011;26(6):433-438.
54. Figueroa JD, Garcia-Closas M, Humphreys M, et al. Associations of common variants at 1p11.2 and 14q24.1 (RAD51L1) with breast cancer risk and heterogeneity by tumor subtype: findings from the Breast Cancer Association Consortium. Hum Mol Genet. 2011;20(23):4693-4706.
55. Bhatti P, Doody MM, Rajaraman P, et al. Novel breast cancer risk alleles and interaction with ionizing radiation among U.S. radiologic technologists. Radiat Res. 2010;173(2):214-224.
56. Vachon CM, Scott CG, Fasching PA, et al. Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2012; 21(7):1156-1166.
57. Navigenics, Inc. The science behind the Navigenics service. Foster City, CA: Navigenics, Inc. http://www.navigenics.com/static/pdf/Navigenics%20White%20Paper.pdf. Accessed July 1, 2012.
58. Macpherson M, Naughton B, Hsu A, et al. Estimating genotype-specific incidence for one or several loci. (White Paper 23-01). Mountain View, CA: 23andme; 2007. https://23andme.https.internapcdn.net/res/pdf/HIC-SXIYiYqX reldAxO5yA-23-01-Estimating-Genotype-Specific- Incidence.pdf. Published September 5, 2007. Updated November 18, 2007. Accessed August 19, 2013.
59. Patel CJ, Sivadas A, Tabassum R, et al. Whole genome sequencing in support of wellness and health maintenance. Genome Med. 2013;5(6):58.
60. Morgan AA, Chen R, Butte AJ. Likelihood ratios for genome medicine. Genome Med. 2010;2(5):30.
61. Ioannidis JP. Why most discovered true associations are inflated. Epidemiology. 2008;19(5):640-648.
62. Kraft P. Curses-winner's and otherwise-in genetic epidemiology. Epidemiology. 2008;19(5):649-651.
63. Zhong H, Prentice RL. Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies. Biostatistics. 2008;9(4):621-634.
64. Vanderweele TJ. Inference for additive interaction under exposure misclassification. Biometrika. 2012;99(2):502-508.
65. National Center for Biotechnology Information. Guidelines for referring to the HapMap populations in publications and presentations. http://hapmap.ncbi.nlm.nih.gov/citinghapmap. html. Updated June 20, 2005. Accessed September 5, 2014.
66. World Health Organization. BMI classification. http://apps.who. int/bmi/index.jsp?introPage=intro-3.html. Updated May 9, 2014. Accessed September 5, 2014.