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

Nguyen, Tuong L, Schmidt, Daniel F., Makalic, Enes, Dite, Gillian S., Stone, Jennifer, Apicella, Carmel, Bui, Minh, MacInnis, Robert J., Odefrey, Fabrice, Cawson, Jennifer N., Treloar, Susan A., Southey, Melissa C., Giles, Graham G. and Hopper, John L. (2013) Explaining variance in the Cumulus mammographic measures that predict breast cancer risk: a twins and sisters study. Cancer Epidemiology Biomarkers and Prevention, 22 12: 2395-2403. doi:10.1158/1055-9965.EPI-13-0481


Author Nguyen, Tuong L
Schmidt, Daniel F.
Makalic, Enes
Dite, Gillian S.
Stone, Jennifer
Apicella, Carmel
Bui, Minh
MacInnis, Robert J.
Odefrey, Fabrice
Cawson, Jennifer N.
Treloar, Susan A.
Southey, Melissa C.
Giles, Graham G.
Hopper, John L.
Title Explaining variance in the Cumulus mammographic measures that predict breast cancer risk: a twins and sisters study
Formatted title
Explaining variance in the Cumulus mammographic measures that predict breast cancer risk: a twins and sisters study
Journal name Cancer Epidemiology Biomarkers and Prevention   Check publisher's open access policy
ISSN 1055-9965
1538-7755
Publication date 2013-01-01
Sub-type Article (original research)
DOI 10.1158/1055-9965.EPI-13-0481
Open Access Status DOI
Volume 22
Issue 12
Start page 2395
End page 2403
Total pages 9
Place of publication Philadelphia, PA, United States
Publisher American Association for Cancer Research
Language eng
Formatted abstract
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. 
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Medicine Publications
 
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