Method Description:
simPEN uses a genetic algorithm to evolve a penetrance model
meeting the specifications of the user. The model is arrived at by minimizing
marginal penetrance variance to simulate a model with minimal main effects while
also optimizing heritability, table variance, and average marginal penetrance
as selected by the user.
simPEN can perform the following:
Generate penetrance tables representing models specified in the configuration file.
Generate a table as above and then generate a case-control dataset using the penetrance
table to assign cases and contrlis.
Generate datasets using a previously defined penetrance table.
The current version of simPEN links to a data simulator, genomeSIM, that will use
the model evolved by simPEN to create case-control datasets as specified.
This program also accepts a datasim file that lists the parameters for
running the simulator.
The genetic algoritm uses a fitness function to evolve a model. The function
evaluates the fitness of each model in the population. The fitness is
dependent on the parameters supplied in the configuration file. Marginal
penetrance variance and heritability always affect fitness. Table variance
and marginal penetrance target only affect fitness when set in configuration file.
Maximum fitness is 1.0. The genetic algorithm terminates when it finds a model
with fitness = 1.0 or the maximum number of generations is reached.
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