title: Explaining variance in the cumulus mammographic measures that predict breast cancer risk: a twins and sisters study.
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 544 MZ and 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%, and 4% for dense area; 7%, 14%, and 4% 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.
  
  
  CONCLUSIONS
  Much remains to be learnt about the genetic and environmental determinants of mammographic density.
authors.affiliation: Authors' Affiliations: Centre for Molecular, Environmental, Genetic, and Analytic Epidemiology, The University of Melbourne; Cancer Epidemiology Centre, Cancer Council Victoria, Carlton; Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville; Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy; and The University of Queensland, Centre for Military and Veterans' Health, Brisbane, Australia.
authors.forename: Tuong L
authors.initials: TL
authors.lastname: Nguyen
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Schmidt
authors.forename: Enes
authors.initials: E
authors.lastname: Makalic
authors.forename: Gillian S
authors.initials: GS
authors.lastname: Dite
authors.forename: Jennifer
authors.initials: J
authors.lastname: Stone
authors.forename: Carmel
authors.initials: C
authors.lastname: Apicella
authors.forename: Minh
authors.initials: M
authors.lastname: Bui
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Macinnis
authors.forename: Fabrice
authors.initials: F
authors.lastname: Odefrey
authors.forename: Jennifer N
authors.initials: JN
authors.lastname: Cawson
authors.forename: Susan A
authors.initials: SA
authors.lastname: Treloar
authors.forename: Melissa C
authors.initials: MC
authors.lastname: Southey
authors.forename: Graham G
authors.initials: GG
authors.lastname: Giles
authors.forename: John L
authors.initials: JL
authors.lastname: Hopper
mesh_terms: D000367:Age Factors; D001315:Australia / Q000453:epidemiology; D015992:Body Mass Index; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology* / Q000235:genetics / Q000473:pathology; D016022:Case-Control Studies; D015331:Cohort Studies; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D008327:Mammography / Q000295:instrumentation / Q000379:methods*; D008875:Middle Aged; D011237:Predictive Value of Tests; D035781:Siblings; D014429:Twins, Dizygotic; D014430:Twins, Monozygotic