Explaining variance in the Cumulus mammographic measures that predict breast cancer risk: A twins and sisters study

Tuong L. Nguyen, Daniel F. Schmidt, Enes Makalic, Gillian S Dite, Jennifer L Stone, Carmel Apicella, Minh Bui, Robert J. MacInnis, Fabrice Odefrey, Jennifer N. Cawson, Susan A. Treloar, Melissa C. Southey, Graham G. Giles, John L. Hopper

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32 Citations (Scopus)


Background: Mammographic density, the area of the mammographic image that appears white or bright, predicts breast cancer risk. We estimated the proportions of variance explained by questionnaire-measured breast cancer risk factors and by unmeasured residual familial factors. Methods: For 544MZand 339 DZ twin pairs and 1,558 non-twin sisters from 1,564 families, mammographic density was measured using the computer-assisted method Cumulus. We estimated associations using multilevel mixed-effects linear regression and studied familial aspects using a multivariate normal model. Results: The proportions of variance explained by age, body mass index (BMI), and other risk factors, respectively, were 4%, 1%, and4% for dense area; 7%, 14%, and4% for percent dense area; and 7%, 40%, and 1% for nondense area. Associations with dense area and percent dense area were in opposite directions than for nondense area. After adjusting for measured factors, the correlations of dense area with percent dense area and nondense area were 0.84 and -0.46, respectively. The MZ, DZ, and sister pair correlations were 0.59, 0.28, and 0.29 for dense area; 0.57, 0.30, and 0.28 for percent dense area; and 0.56, 0.27, and 0.28 for nondense area (SE = 0.02, 0.04, and 0.03, respectively). Conclusions: Under the classic twin model, 50% to 60% (SE=5%) of the variance of mammographic density measures that predict breast cancer risk are due to undiscovered genetic factors, and the remainder to as yet unknown individual-specific, nongenetic factors. Impact: Much remains to be learnt about the genetic and environmental determinants of mammographic density.

Original languageEnglish
Pages (from-to)2395-2403
Number of pages9
JournalCancer Epidemiology, Biomarkers and Prevention
Issue number12
Publication statusPublished - Dec 2013
Externally publishedYes

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