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

description: 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 &lt; 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.