The Heritability of Mammographically Dense and Nondense Breast Tissue

Cancer Epidemiology Biomarkers and Prevention - Tập 15 Số 4 - Trang 612-617 - 2006
Jennifer Stone1, Gillian S. Dite1, Anoma Gunasekara2, Dallas R. English3, Margaret McCredie4, Graham G. Giles3, Jennifer Cawson5, Robert A. Hegele6, Anna M. Chiarelli7, Martin J. Yaffe8, Norman F. Boyd2, John L. Hopper1
11Centre for Genetic Epidemiology and
23The Division of Epidemiology and Statistics, Ontario Cancer Institute;
36Centre for Cancer Epidemiology, The Cancer Council of Victoria, Victoria, Australia;
47Department of Preventive and Social Medicine, University of Otago, Otago, New Zealand; and
52St. Vincent's BreastScreen, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia;
68Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, London, Ontario, Canada
74Division of Preventive Oncology, Cancer Care Ontario;
85Imaging Research, Sunnybrook and Women's College Hospital, Toronto, Ontario, Canada;

Tóm tắt

Abstract Background: Percent mammographic density (PMD) is a risk factor for breast cancer. Our previous twin study showed that the heritability of PMD was 63%. This study determined the heritabilities of the components of PMD, the areas of dense and nondense tissue in the mammogram. Methods: We combined two twin studies comprising 571 monozygous and 380 dizygous twin pairs recruited from Australia and North America. Dense and nondense areas were measured using a computer-assisted method, and information about potential determinants was obtained by questionnaire. Under the assumptions of the classic twin model, we estimated the heritability of the log dense area and log nondense area and the genetic and environmental contributions to the covariance between the two traits, using maximum likelihood theory and the statistical package FISHER. Results: After adjusting for measured determinants, for each of the log dense area and the log nondense area, the monozygous correlations were greater than the dizygous correlations. Heritability was estimated to be 65% (95% confidence interval, 60-70%) for dense area and 66% (95% confidence interval, 61-71%) for nondense area. The correlations (SE) between the two adjusted traits were −0.35 (0.023) in the same individual, −0.26 (0.026) across monozygous pairs, and −0.14 (0.034) across dizygous pairs. Conclusion: Genetic factors may play a large role in explaining variation in the mammographic areas of both dense and nondense tissue. About two thirds of the negative correlation between dense and nondense area is explained by the same genetic factors influencing both traits, but in opposite directions. (Cancer Epidemiol Biomarkers Prev 2006;15(4):612–7)

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Tài liệu tham khảo

Boyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ. Mammographic densities and breast cancer risk. Cancer Epidemiol Biomarkers Prev 1998;7:1133–44.

Byrne C, Schairer C, Wolfe J, et al. Mammographic features and breast cancer risk: effects with time, age, and menopause status. J Natl Cancer Inst 1995;87:1622–9.

Byng JW, Yaffe MJ, Jong RA, et al. Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics 1998;18:1587–98.

Wolfe JN, Albert S, Belle S, Salane M. Familial influences on breast parenchymal patterns. Cancer 1980;46:2433–7.

Kaprio J, Alanko A, Kivisaari L, Standertskjold-Nordenstam CG. Mammographic patterns in twin pairs discordant for breast cancer. Br J Radiol 1987;60:459–62.

Pankow JS, Vachon CM, Kuni CC, et al. Genetic analysis of mammographic breast density in adult women: evidence of a gene effect. J Natl Cancer Inst 1997;89:549–56.

Vachon CM, King RA, Atwood LD, Kuni CC, Sellers TA. Preliminary sibpair linkage analysis of percent mammographic density. J Natl Cancer Inst 1999;91:1778–9.

Boyd NF, Dite GS, Stone J, et al. Heritability of mammographic density, a risk factor for breast cancer. N Engl J Med 2002;347:886–94.

Hopper JL. The Australian Twin Registry. Twin Res 2002;5:329–36.

Goldsmith H. A zygosity questionnaire for young twins; a research note. Behav Genet 1991;21:257–69.

Spitzer E, Moutier R, Reed T, et al. Comparative diagnoses of twin zygosity by SSLP variant analysis, questionnaire, and dermatoglyphic analysis. Behav Genet 1996;26:55–63.

Torgersen S. The determination of twin zygosity by means of a mailed questionnaire. Acta Genet Med Gemellol (Roma) 1979;28:225–36.

Lange K, Boehnke M, Weeks D. Programs for pedigree analysis. Los Angeles: Department of Biomathematics, University of California; 1987.

Hopper JL, Mathews JD. Extensions to multivariate normal models for pedigree analysis. Ann Hum Genet 1982;46:373–83.

Akaike H. A new look at the statistical model identification. IEEE Trans Automatic Control 1974;19:716–22.

Fisher R. The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinburgh 1918;52:399–433.

Lange K, Boehnke M. Extensions to pedigree analysis. IV. Covariance components models for multivariate traits. Am J Med Genet 1983;14:513–24.

Seeman E, Hopper JL, Young N, Goss P, Tsalamandris C. Do genetic factors explain associations between muscle strength, lean mass, and bone density? A twin study. Am Physiol Soc 1996;96:320–7.

Hopper JL, Visscher PM. Genetic correlations and covariances. In: Elston RC, Olson JM, Palmer L, editors. Biostatistical genetics and genetic epidemiology. Milton, Queensland (Australia): John Wiley & Sons Ltd.; 2002. p. 330.

Hopper JL, Visscher PM. Variance component analysis. In: Elston RC, Olson JM, Palmer L, editors. Biostatistical genetics and genetic epidemiology. Milton, Queensland (Australia); John Wiley & Sons Ltd.; 2002. p. 779–80.