||Pearce CL, Rossing MA, Lee AW, Ness RB, Webb PM, for Australian Cancer Study (Ovarian Cancer), Australian Ovarian Cancer Study Group, Chenevix-Trench G, Jordan SM, Stram DA, Chang-Claude J, Hein R, Nickels S, Lurie G, Thompson PJ, Carney ME, Goodman MT, Moysich K, Hogdall E, Jensen A, Goode EL, Fridley BL, Cunningham JM, Vierkant RA, Weber RP, Ziogas A, Anton-Culver H, Gayther SA, Gentry-Maharaj A, Menon U, Ramus SJ, Brinton L, Wentzensen N, Lissowska J, Garcia-Closas M, Massuger LF, Kiemeney LA, Van Altena AM, Aben KK, Berchuck A, Doherty JA, Iversen E, McGuire V, Moorman PG, Pharoah P, Pike MC, Risch H, Sieh W, Stram DO, Terry KL, Whittemore A, Wu AH, Schildkraut JM, Kjaer SK, Ovarian Cancer Association Consortium
||BACKGROUND: There are several well-established environmental risk factors for ovarian cancer, and recent genome-wide association studies have also identified six variants that influence disease risk. However, the interplay between such risk factors and susceptibility loci has not been studied. METHODS: Data from 14 ovarian cancer case-control studies were pooled, and stratified analyses by each environmental risk factor with tests for heterogeneity were conducted to determine the presence of interactions for all histologic subtypes. A genetic "risk score" was created to consider the effects of all six variants simultaneously. A multivariate model was fit to examine the association between all environmental risk factors and genetic risk score on ovarian cancer risk. RESULTS: Among 7,374 controls and 5,566 cases, there was no statistical evidence of interaction between the six SNPs or genetic risk score and the environmental risk factors on ovarian cancer risk. In a main effects model, women in the highest genetic risk score quartile had a 65% increased risk of ovarian cancer compared with women in the lowest [95% confidence interval (CI), 1.48-1.84]. Analyses by histologic subtype yielded risk differences across subtype for endometriosis (Phet < 0.001), parity (Phet < 0.01), and tubal ligation (Phet = 0.041). CONCLUSIONS: The lack of interactions suggests that a multiplicative model is the best fit for these data. Under such a model, we provide a robust estimate of the effect of each risk factor that sets the stage for absolute risk prediction modeling that considers both environmental and genetic risk factors. Further research into the observed differences in risk across histologic subtype is warranted.