Table 1 |
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| Summary of the objectives and design of simulations 1-3 | |||
| Simulation 1 | Simulation 2 | Simulation 3 | |
| Objective | To compare RF VIMs for main and interaction effect detection. | To compare RF measures with p-values from logistic regression for main and interaction effect detection. | Examine RF performance in presence of realistic patterns of LD and MAF. |
| Independent SNPs | Yes | Yes | No (LD) |
| # Total Loci (p) | 10, 100, 500, 1000 | 10, 100, 500, 1000 | Fixed at 1000 |
| # Causal Loci (k) | 4 | 2 | 2 |
| MAF | Fixed at 0.1, 0.2, 0.3, or 0.4 | Fixed at 0.3 | Varies (0.01–0.50) |
| # Model Scenarios | 5 | 3 | 4 |
| Description | Varying effect sizes, HX1X22 vs. HX3X42 | Two interacting SNPs with 0, 1, or 2 having main effects. | Causal SNPs chosen in blocks of strong vs. weak LD with non-causal SNPs. |
| Phenotype Generation | Phenotype is a dichotomized quantitative (normally distributed) trait. | Phenotype is based on direct penetrance functions. | Phenotypes are generated as in Simulation 1. |
Winham et al. BMC Bioinformatics 2012 13:164 doi:10.1186/1471-2105-13-164
