TY - JOUR
T1 - Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk
AU - Vachon, Celine M.
AU - Scott, Christopher G.
AU - Tamimi, Rulla M.
AU - Thompson, Deborah J.
AU - Fasching, Peter A.
AU - Stone, Jennifer
AU - Southey, Melissa C.
AU - Winham, Stacey
AU - Lindström, Sara
AU - Lilyquist, Jenna
AU - Giles, Graham G.
AU - Milne, Roger L.
AU - MacInnis, Robert J.
AU - Baglietto, Laura
AU - Li, Jingmei
AU - Czene, Kamila
AU - Bolla, Manjeet K.
AU - Wang, Qin
AU - Dennis, Joe
AU - Haeberle, Lothar
AU - Eriksson, Mikael
AU - Kraft, Peter
AU - Luben, Robert
AU - Wareham, Nick
AU - Olson, Janet E.
AU - Norman, Aaron
AU - Polley, Eric C.
AU - Maskarinec, Gertraud
AU - Le Marchand, Loic
AU - Haiman, Christopher A.
AU - Hopper, John L.
AU - Couch, Fergus J.
AU - Easton, Douglas F.
AU - Hall, Per
AU - Chatterjee, Nilanjan
AU - Garcia-Closas, Montse
N1 - Funding Information:
This work was supported by the National Cancer Institute (R01 CA128931, R01 CA122340, R01 CA128978, R01 CA97396, P50 CA116201, R01 CA240386, K24 CA169004, R21 CA179442, P01 CA154292, P01CA87969, R01 CA085265, UM1CA176726, UM1CA186107, and U01 CA164973) and the Breast Cancer Research Foundation. BBCC was supported in part by the ELAN program of the Medical Faculty, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg. Funding for the genotyping of BBCC and MCBCS as well as the iCOGS Illumina array is provided by grants from the EU FP7 programme (COGS) and from Cancer Research UK. Collaborative Oncological Gene-environment Study (COGS) enabled the genotyping for this study. Funding for the BCAC component is provided by grants from the EU FP7 programme (COGS) and from Cancer Research UK. BCAC is funded by Cancer Research UK [C1287/A16563] and by the European Community’s Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS) and by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements 633784 (B-CAST) and 634935 (BRIDGES). The SASBAC study was supported by the Märit and Hans Rausing’s Initiative Against Breast Cancer, the National Institutes of Health (RO1 CA58427), the Agency for Science, Technology and Research (A*STAR; Singapore), and the Swedish Research Council. Jingmei Li is a recipient of a National Research Foundation Singapore Fellowship (NRF-NRFF2017–02). The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian National Health and Medical Research Council grants 209057 and 396414 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.
Funding Information:
We would like to thank the participants and staff of the NHS and NHSII for their valuable contributions as well as the state cancer registries of the following states for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, HI, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. This work was supported by the National Cancer Institute (R01 CA128931, R01 CA122340, R01 CA128978, R01 CA97396, P50 CA116201, R01 CA240386, K24 CA169004, R21 CA179442, P01 CA154292, P01CA87969, R01 CA085265, UM1CA176726, UM1CA186107, and U01 CA164973) and the Breast Cancer Research Foundation. BBCC was supported in part by the ELAN program of the Medical Faculty, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg. Funding for the genotyping of BBCC and MCBCS as well as the iCOGS Illumina array is provided by grants from the EU FP7 programme (COGS) and from Cancer Research UK. Collaborative Oncological Gene-environment Study (COGS) enabled the genotyping for this study. Funding for the BCAC component is provided by grants from the EU FP7 programme (COGS) and from Cancer Research UK. BCAC is funded by Cancer Research UK [C1287/A16563] and by the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS) and by the European Union's Horizon 2020 Research and Innovation Programme under grant agreements 633784 (B-CAST) and 634935 (BRIDGES). The SASBAC study was supported by the Märit and Hans Rausing's Initiative Against Breast Cancer, the National Institutes of Health (RO1 CA58427), the Agency for Science, Technology and Research (A∗STAR; Singapore), and the Swedish Research Council. Jingmei Li is a recipient of a National Research Foundation Singapore Fellowship (NRF-NRFF2017-02). The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian National Health and Medical Research Council grants 209057 and 396414 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.
Publisher Copyright:
© 2019 The Author(s).
PY - 2019/5/22
Y1 - 2019/5/22
N2 - Background: Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk. Methods: Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies. Results: Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38-1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28-1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45-1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile. Conclusions: The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.
AB - Background: Mammographic breast density, adjusted for age and body mass index, and a polygenic risk score (PRS), comprised of common genetic variation, are both strong risk factors for breast cancer and increase discrimination of risk models. Understanding their joint contribution will be important to more accurately predict risk. Methods: Using 3628 breast cancer cases and 5126 controls of European ancestry from eight case-control studies, we evaluated joint associations of a 77-single nucleotide polymorphism (SNP) PRS and quantitative mammographic density measures with breast cancer. Mammographic percent density and absolute dense area were evaluated using thresholding software and examined as residuals after adjusting for age, 1/BMI, and study. PRS and adjusted density phenotypes were modeled both continuously (per 1 standard deviation, SD) and categorically. We fit logistic regression models and tested the null hypothesis of multiplicative joint associations for PRS and adjusted density measures using likelihood ratio and global and tail-based goodness of fit tests within the subset of six cohort or population-based studies. Results: Adjusted percent density (odds ratio (OR) = 1.45 per SD, 95% CI 1.38-1.52), adjusted absolute dense area (OR = 1.34 per SD, 95% CI 1.28-1.41), and the 77-SNP PRS (OR = 1.52 per SD, 95% CI 1.45-1.59) were associated with breast cancer risk. There was no evidence of interaction of the PRS with adjusted percent density or dense area on risk of breast cancer by either the likelihood ratio (P > 0.21) or goodness of fit tests (P > 0.09), whether assessed continuously or categorically. The joint association (OR) was 2.60 in the highest categories of adjusted PD and PRS and 0.34 in the lowest categories, relative to women in the second density quartile and middle PRS quintile. Conclusions: The combined associations of the 77-SNP PRS and adjusted density measures are generally well described by multiplicative models, and both risk factors provide independent information on breast cancer risk.
KW - Breast cancer risk
KW - Breast density
KW - Genetic variation
KW - Polygenic risk score
KW - Risk models
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U2 - 10.1186/s13058-019-1138-8
DO - 10.1186/s13058-019-1138-8
M3 - Article
C2 - 31118087
AN - SCOPUS:85066484413
VL - 21
JO - Breast Cancer Research
JF - Breast Cancer Research
SN - 1465-5411
IS - 1
M1 - 68
ER -