abstract
- The association analysis between single nucleotide polymorphisms (SNPs) and disease or endpoint in genome-wide association studies (GWAS) has been considered as a powerful strategy for investigating genetic susceptibility and for identifying significant biomarkers. The statistical analysis approaches with simulated data have been widely used to review experimental designs and performance measurements. In recent years, a number of authors have proposed methods for the simulation of biological data in the genomic field. However, these methods use large-scale genomic data as a reference to simulate experiments, which may limit the use of the methods in the case where the data in specific studies are not available. Few methods use experimental results or observed parameters for simulation. The goal of this study is to develop a Web application called SITDEM to simulate disease/endpoint models in three different approaches based on only parameters observed in GWAS. In our simulation, a key task is to compute the probability of genotypes. Based on that, we randomly sample simulation data. Simulation results are shown as a function of p-value against odds ratio or relative risk of a SNP in dominant and recessive models. Our simulation results show the potential of SITDEM for simulating genotype data. SITDEM could be particularly useful for investigating the relationship among observed parameters for target SNPs and for estimating the number of variables (SNPs) required to result in significant p-values in multiple comparisons. The proposed simulation tool is freely available at http://www.snpmodel.com.