Due to the multiple loci control nature of complex phenotypes, there is great interest
to test markers simultaneously instead of one by one. In this paper, we compare three model
selection methods for genome wide association studies using simulations: the Stochastic Search
Variable Selection (SSVS), the Least Absolute Shrinkage and Selection Operator (LASSO) and the
Elastic Net. We also apply the three methods to identify genetic variants that are associated with
daunorubicin-induced cytotoxicity. The simulation studies were performed by using the genotype
data of 60 unrelated individuals from the CEU population in the Hapmap project. For the cytotoxicity
data, we used 3,967,790 markers across the whole genome for 56 unrelated individuals from the
CEU population. Using Sure Independence Screening as the pre-screening procedure, the SSVS gives
a small model while the LASSO gives an intermediate sized model and the Elastic Net provides a
large model. The three models share many common markers although the model sizes are different.
The model sizes are subject to various cutoffs and parameters. The SSVS outperforms the LASSO
and the Elastic Net in simulation studies. We also demonstrate the ability of the SSVS, the LASSO,
and the Elastic Net to handle the situation when the number of markers is larger than the number
of samples.