The heritability of breast cancer: a Bayesian correlated frailty model applied to Swedish twins data.

The aim of this study was to investigate the role of genes and environment in susceptibility to breast cancer and to give an estimate of heritability in the propensity to develop the disease. To do this we applied an interdisciplinary approach, merging models developed in the field of demography and survival analysis - so-called frailty models - and models coming from quantitative genetics and epidemiology, namely genetic models. In our study, the inferential problem was solved in a Bayesian framework and the numerical work was carried out using MCMC methods. We used the special information coming from twin data, particularly breast cancer data, from the Swedish Twin Register. The application of a correlated log-normal frailty model leads to a very large estimate of the population heterogeneity (sigma = 6.7), and relatively small correlations between co-twins' frailties - around 0.3 for monozygotic and 0.1 for dizygotic twins. Comparing three different genetic models (an ACE, an AE and an ADE model), we furthermore concluded that genetic effects would explain globally almost 30% of the total variability of propensity to breast cancer. Environmental effects would be predominant in determining breast cancer susceptibility and these effects would be primarily individual-specific, that is, non-shared effects. Finally, a model which includes dominance genetic effects (ADE model) is preferred for genetic and statistical reasons.