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Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

Authors :
Guo, Yan
Warren Andersen, Shaneda
Shu, Xiao-Ou
Michailidou, Kyriaki
Bolla, Manjeet K.
Wang, Qin
Garcia-Closas, Montserrat
Milne, Roger L.
Schmidt, Marjanka K.
Chang-Claude, Jenny
Dunning, Allison
Bojesen, Stig E.
Ahsan, Habibul
Aittomäki, Kristiina
Andrulis, Irene L.
Anton-Culver, Hoda
Arndt, Volker
Beckmann, Matthias W.
Beeghly-Fadiel, Alicia
Benitez, Javier
Bogdanova, Natalia V.
Bonanni, Bernardo
Børresen-Dale, Anne-Lise
Brand, Judith
Brauch, Hiltrud
Brenner, Hermann
Brüning, Thomas
Burwinkel, Barbara
Casey, Graham
Chenevix-Trench, Georgia
Couch, Fergus J.
Cox, Angela
Cross, Simon S.
Czene, Kamila
Devilee, Peter
Dörk, Thilo
Dumont, Martine
Fasching, Peter A.
Figueroa, Jonine
Flesch-Janys, Dieter
Fletcher, Olivia
Flyger, Henrik
Fostira, Florentia
Gammon, Marilie
Giles, Graham G.
Guénel, Pascal
Haiman, Christopher A.
Hamann, Ute
Hooning, Maartje J.
Hopper, John L.
Jakubowska, Anna
Jasmine, Farzana
Jenkins, Mark
John, Esther M.
Johnson, Nichola
Jones, Michael E.
Kabisch, Maria
Kibriya, Muhammad
Knight, Julia A.
Koppert, Linetta B.
Kosma, Veli-Matti
Kristensen, Vessela
Le Marchand, Loic
Lee, Eunjung
Li, Jingmei
Lindblom, Annika
Luben, Robert
Lubinski, Jan
Malone, Kathi E.
Mannermaa, Arto
Margolin, Sara
Marme, Frederik
McLean, Catriona
Meijers-Heijboer, Hanne
Meindl, Alfons
Neuhausen, Susan L.
Nevanlinna, Heli
Neven, Patrick
Olson, Janet E.
Perez, Jose I. A.
Perkins, Barbara
Peterlongo, Paolo
Phillips, Kelly-Anne
Pylkäs, Katri
Rudolph, Anja
Santella, Regina
Sawyer, Elinor J.
Schmutzler, Rita K.
Seynaeve, Caroline
Shah, Mitul
Shrubsole, Martha J.
Southey, Melissa C.
Swerdlow, Anthony J.
Toland, Amanda E.
Tomlinson, Ian
Torres, Diana
Truong, Thérèse
Ursin, Giske
Van Der Luijt, Rob B.
Verhoef, Senno
Whittemore, Alice S.
Winqvist, Robert
Zhao, Hui
Zhao, Shilin
Hall, Per
Simard, Jacques
Kraft, Peter
Pharoah, Paul
Hunter, David
Easton, Douglas F.
Zheng, Wei
Source :
Guo, Y., S. Warren Andersen, X. Shu, K. Michailidou, M. K. Bolla, Q. Wang, M. Garcia-Closas, et al. 2016. “Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent.” PLoS Medicine 13 (8): e1002105. doi:10.1371/journal.pmed.1002105. http://dx.doi.org/10.1371/journal.pmed.1002105.
Publication Year :
2016
Publisher :
Public Library of Science, 2016.

Abstract

Background: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. Methods: We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases = 46,325, controls = 42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. Results: In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR] = 0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56–0.75, p = 3.32 × 10−10). The associations were similar for both premenopausal (OR = 0.44, 95% CI:0.31–0.62, p = 9.91 × 10−8) and postmenopausal breast cancer (OR = 0.57, 95% CI: 0.46–0.71, p = 1.88 × 10−8). This association was replicated in the data from the DRIVE consortium (OR = 0.72, 95% CI: 0.60–0.84, p = 1.64 × 10−7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. Conclusions: BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer.

Details

Language :
English
ISSN :
15491277
Database :
Digital Access to Scholarship at Harvard (DASH)
Journal :
Guo, Y., S. Warren Andersen, X. Shu, K. Michailidou, M. K. Bolla, Q. Wang, M. Garcia-Closas, et al. 2016. “Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent.” PLoS Medicine 13 (8): e1002105. doi:10.1371/journal.pmed.1002105. http://dx.doi.org/10.1371/journal.pmed.1002105.
Publication Type :
Academic Journal
Accession number :
edshld.1.29407887
Document Type :
Journal Article
Full Text :
https://doi.org/10.1371/journal.pmed.1002105