TY - JOUR
T1 - Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk
T2 - Candidate Genes, Obesity, and Hormone-Related Risk Factors
AU - Usset, Joseph L
AU - Raghavan, Rama
AU - Tyrer, Jonathan P
AU - McGuire, Valerie
AU - Sieh, Weiva
AU - Webb, Penelope
AU - Chang-Claude, Jenny
AU - Rudolph, Anja
AU - Anton-Culver, Hoda
AU - Berchuck, Andrew
AU - Brinton, Louise
AU - Cunningham, Julie M
AU - DeFazio, Anna
AU - Doherty, Jennifer A
AU - Edwards, Robert P
AU - Gayther, Simon A
AU - Gentry-Maharaj, Aleksandra
AU - Goodman, Marc T
AU - Høgdall, Estrid
AU - Jensen, Allan
AU - Johnatty, Sharon E
AU - Kiemeney, Lambertus A
AU - Kjaer, Susanne K
AU - Larson, Melissa C
AU - Lurie, Galina
AU - Massuger, Leon
AU - Menon, Usha
AU - Modugno, Francesmary
AU - Moysich, Kirsten B
AU - Ness, Roberta B
AU - Pike, Malcolm C
AU - Ramus, Susan J
AU - Rossing, Mary Anne
AU - Rothstein, Joseph
AU - Song, Honglin
AU - Thompson, Pamela J
AU - van den Berg, David J
AU - Vierkant, Robert A
AU - Wang-Gohrke, Shan
AU - Wentzensen, Nicolas
AU - Whittemore, Alice S
AU - Wilkens, Lynne R
AU - Wu, Anna H
AU - Yang, Hannah
AU - Pearce, Celeste Leigh
AU - Schildkraut, Joellen M
AU - Pharoah, Paul
AU - Goode, Ellen L
AU - Fridley, Brooke L
AU - Ovarian Cancer Association Consortium and the Australian Cancer Study
N1 - ©2016 American Association for Cancer Research.
PY - 2016/5
Y1 - 2016/5
N2 - Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene?environment interactions related to hormone-related risk factors could differ between obese and nonobese women. Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormonerelated factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case?control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10-6) and ESR1 (rs12661437, endometriosis, histology=all, P=1.5×10-5). The most notable obesity?gene?hormone risk factor interaction was within INSR (rs113759408, parity, histology=endometrioid, P= 8.8 × 10-6). Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions. Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factorsmay vary EOC susceptibility.
AB - Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene?environment interactions related to hormone-related risk factors could differ between obese and nonobese women. Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormonerelated factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case?control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10-6) and ESR1 (rs12661437, endometriosis, histology=all, P=1.5×10-5). The most notable obesity?gene?hormone risk factor interaction was within INSR (rs113759408, parity, histology=endometrioid, P= 8.8 × 10-6). Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions. Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factorsmay vary EOC susceptibility.
KW - Journal Article
U2 - 10.1158/1055-9965.epi-15-1039
DO - 10.1158/1055-9965.epi-15-1039
M3 - Journal article
C2 - 26976855
SN - 1055-9965
VL - 25
SP - 780
EP - 790
JO - Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
JF - Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
IS - 5
ER -
