From: SNP interaction detection with Random Forests in high-dimensional genetic data
 | 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. |