Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent
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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. / 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.
In: P L o S Medicine (Online), Vol. 13, No. 8, e1002105, 08.2016, p. 1-18.Research output: Contribution to journal › Journal article › peer-review
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TY - JOUR
T1 - Genetically Predicted Body Mass Index and Breast Cancer Risk
T2 - Mendelian Randomization Analyses of Data from 145,000 Women of European Descent
AU - Guo, Yan
AU - Warren Andersen, Shaneda
AU - Shu, Xiao-Ou
AU - Michailidou, Kyriaki
AU - Bolla, Manjeet K
AU - Wang, Qin
AU - Garcia-Closas, Montserrat
AU - Milne, Roger L
AU - Schmidt, Marjanka K
AU - Chang-Claude, Jenny
AU - Dunning, Allison
AU - Bojesen, Stig E
AU - Ahsan, Habibul
AU - Aittomäki, Kristiina
AU - Andrulis, Irene L
AU - Anton-Culver, Hoda
AU - Arndt, Volker
AU - Beckmann, Matthias W
AU - Beeghly-Fadiel, Alicia
AU - Benitez, Javier
AU - Bogdanova, Natalia V
AU - Bonanni, Bernardo
AU - Børresen-Dale, Anne-Lise
AU - Brand, Judith
AU - Brauch, Hiltrud
AU - Brenner, Hermann
AU - Brüning, Thomas
AU - Burwinkel, Barbara
AU - Casey, Graham
AU - Chenevix-Trench, Georgia
AU - Couch, Fergus J
AU - Cox, Angela
AU - Cross, Simon S
AU - Czene, Kamila
AU - Devilee, Peter
AU - Dörk, Thilo
AU - Dumont, Martine
AU - Fasching, Peter A
AU - Figueroa, Jonine
AU - Flesch-Janys, Dieter
AU - Fletcher, Olivia
AU - Flyger, Henrik
AU - Fostira, Florentia
AU - Gammon, Marilie
AU - Giles, Graham G
AU - Guénel, Pascal
AU - Haiman, Christopher A
AU - Hamann, Ute
AU - Hooning, Maartje J
AU - Hopper, John L
AU - Jakubowska, Anna
AU - Jasmine, Farzana
AU - Jenkins, Mark
AU - John, Esther M
AU - Johnson, Nichola
AU - Jones, Michael E
AU - Kabisch, Maria
AU - Kibriya, Muhammad
AU - Knight, Julia A
AU - Koppert, Linetta B
AU - Kosma, Veli-Matti
AU - Kristensen, Vessela
AU - Le Marchand, Loic
AU - Lee, Eunjung
AU - Li, Jingmei
AU - Lindblom, Annika
AU - Luben, Robert
AU - Lubinski, Jan
AU - Malone, Kathi E
AU - Mannermaa, Arto
AU - Margolin, Sara
AU - Marme, Frederik
AU - McLean, Catriona
AU - Meijers-Heijboer, Hanne
AU - Meindl, Alfons
AU - Neuhausen, Susan L
AU - Nevanlinna, Heli
AU - Neven, Patrick
AU - Olson, Janet E
AU - Perez, Jose I A
AU - Perkins, Barbara
AU - Peterlongo, Paolo
AU - Phillips, Kelly-Anne
AU - Pylkäs, Katri
AU - Rudolph, Anja
AU - Santella, Regina
AU - Sawyer, Elinor J
AU - Schmutzler, Rita K
AU - Seynaeve, Caroline
AU - Shah, Mitul
AU - Shrubsole, Martha J
AU - Southey, Melissa C
AU - Swerdlow, Anthony J
AU - Toland, Amanda E
AU - Tomlinson, Ian
AU - Torres, Diana
AU - Truong, Thérèse
AU - Ursin, Giske
AU - Van Der Luijt, Rob B
AU - Verhoef, Senno
AU - Whittemore, Alice S
AU - Winqvist, Robert
AU - Zhao, Hui
AU - Zhao, Shilin
AU - Hall, Per
AU - Simard, Jacques
AU - Kraft, Peter
AU - Pharoah, Paul
AU - Hunter, David
AU - Easton, Douglas F
AU - Zheng, Wei
PY - 2016/8
Y1 - 2016/8
N2 - 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.
AB - 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.
KW - Journal Article
U2 - 10.1371/journal.pmed.1002105
DO - 10.1371/journal.pmed.1002105
M3 - Journal article
C2 - 27551723
VL - 13
SP - 1
EP - 18
JO - P L o S Medicine (Online)
JF - P L o S Medicine (Online)
SN - 1549-1277
IS - 8
M1 - e1002105
ER -
ID: 171998658