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. 2016 Aug 23;13(8):e1002105.
doi: 10.1371/journal.pmed.1002105. eCollection 2016 Aug.

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

Yan Guo  1 Shaneda Warren Andersen  2 Xiao-Ou Shu  2 Kyriaki Michailidou  3 Manjeet K Bolla  3 Qin Wang  3 Montserrat Garcia-Closas  4   5 Roger L Milne  6   7 Marjanka K Schmidt  8 Jenny Chang-Claude  9   10 Allison Dunning  11 Stig E Bojesen  12   13   14 Habibul Ahsan  15 Kristiina Aittomäki  16 Irene L Andrulis  17   18 Hoda Anton-Culver  19 Volker Arndt  20 Matthias W Beckmann  15 Alicia Beeghly-Fadiel  2 Javier Benitez  21   22 Natalia V Bogdanova  23 Bernardo Bonanni  24 Anne-Lise Børresen-Dale  25   26 Judith Brand  27 Hiltrud Brauch  28   29   30 Hermann Brenner  20   28   31 Thomas Brüning  32 Barbara Burwinkel  33   34 Graham Casey  35 Georgia Chenevix-Trench  36 Fergus J Couch  37 Angela Cox  38 Simon S Cross  11 Kamila Czene  27 Peter Devilee  39 Thilo Dörk  40 Martine Dumont  41 Peter A Fasching  42   43 Jonine Figueroa  44 Dieter Flesch-Janys  45   46 Olivia Fletcher  5 Henrik Flyger  47 Florentia Fostira  48 Marilie Gammon  49 Graham G Giles  6   7 Pascal Guénel  50   51 Christopher A Haiman  35 Ute Hamann  52 Maartje J Hooning  53 John L Hopper  7 Anna Jakubowska  54 Farzana Jasmine  15 Mark Jenkins  7 Esther M John  55   56 Nichola Johnson  5 Michael E Jones  4 Maria Kabisch  52 Muhammad Kibriya  15 Julia A Knight  57   58 Linetta B Koppert  53 Veli-Matti Kosma  59   60   61 Vessela Kristensen  25   26   62 Loic Le Marchand  63 Eunjung Lee  35 Jingmei Li  27 Annika Lindblom  64 Robert Luben  65 Jan Lubinski  54 Kathi E Malone  66 Arto Mannermaa  59   60   61 Sara Margolin  67 Frederik Marme  68   69 Catriona McLean  70 Hanne Meijers-Heijboer  71 Alfons Meindl  72 Susan L Neuhausen  73 Heli Nevanlinna  74 Patrick Neven  75 Janet E Olson  76 Jose I A Perez  77 Barbara Perkins  78 Paolo Peterlongo  79 Kelly-Anne Phillips  80   81   82 Katri Pylkäs  83 Anja Rudolph  9 Regina Santella  84   85 Elinor J Sawyer  86 Rita K Schmutzler  87   88   89   90 Caroline Seynaeve  53 Mitul Shah  78 Martha J Shrubsole  2 Melissa C Southey  91 Anthony J Swerdlow  4   92 Amanda E Toland  93 Ian Tomlinson  94 Diana Torres  52 Thérèse Truong  50   51 Giske Ursin  95 Rob B Van Der Luijt  96 Senno Verhoef  8 Alice S Whittemore  56 Robert Winqvist  83   97 Hui Zhao  98   99 Shilin Zhao  1 Per Hall  27 Jacques Simard  41 Peter Kraft  100   101 Paul Pharoah  3   78 David Hunter  100   101 Douglas F Easton  3   78 Wei Zheng  2
Affiliations

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

Yan Guo et al. PLoS Med. .

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Meta-analysis of the association between genetically predicted BMI and breast cancer risk in the BCAC.
The summary OR was calculated by combining individual analysis results from each study in BCAC (p for heterogeneity = 0.06).
Fig 2
Fig 2. Sensitivity analyses using pooled data for associations between genetically predicted BMI and breast cancer risk in the BCAC.
(A) Adjusted for age, study sites, and the first eight principal components. (b) Adjusted for age, study sites, the first eight principal components, and additional breast cancer risk factors: age at menarche, parity, use of contraceptive, use of hormone replacement therapy, breast feeding, and smoking status. Weighted: the BMI-GS was constructed using the additive model weighted by external beta reported from previous literatures. Unweighted: the BMI-GS was constructed using the additive model without any weight.

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