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Table 1 Summary of the objectives and design of simulations 1-3

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.