"Population lab": the creation of virtual populations for genetic epidemiology research.
BACKGROUND
Studies of familial aggregation of disease routinely use linked population registers to construct retrospective cohorts. Although such resources have provided numerous estimates of familial risk, little is known regarding the sensitivity of the estimates to assumed disease models, changing demographics and incidence, and incompleteness of the data. Furthermore, there are no standard tools for testing the validity of estimates from standard epidemiologic designs and from new analytic strategies using register data.


METHODS
We present a method and a software package for simulating realistic populations of related individuals, using easily available vital statistics (population counts and fertility and mortality rates). The virtual population is stored in a pedigree file, allowing for easy retrieval of relatives and family structures. We simulate breast cancer in our population using age-specific incidence rates.


RESULTS
The Swedish population is simulated as dynamically evolving over the calendar period 1955-2002. The simulated and real population agree well on important features such as age profile, sibship size distribution, and average age at first birth. Using breast cancer as an example, we present several models of familial disease aggregation and show that the parameters used in the simulations are faithfully estimated. In addition, we illustrate how our simulated population provides insight into how incomplete family history in real register data can affect estimates of familial risk.


CONCLUSIONS
This simulation method can be used to investigate various underlying models of disease aggregation in families and enhance the development of optimal approaches for family studies. The software package, Population Lab, is available for free download (http://www.meb.ki.se/ approximately marrei/software/poplab/ and http://cran.at.r-project.org/).
Leu
Monica
M
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. monica.leu@ki.se
Czene
Kamila
K
Reilly
Marie
M
D016022:Case-Control Studies; D003198:Computer Simulation; D003710:Demography; D016021:Epidemiologic Studies; D005260:Female; D020022:Genetic Predisposition to Disease / Q000453:epidemiology*; D006801:Humans; D008297:Male; D008954:Models, Biological; D010375:Pedigree; D012984:Software; D013548:Sweden