SNPSetSimulations-package: Simulation of genotypic profiles and binary phenotypes for...

In fhebert/SNPSetSimulations: Simulation of genotypic profiles and binary phenotypes for GWAS

Simulation of genotypic profiles and binary phenotypes for GWAS

Description

The SNPSetSimulations package provides functions to generate genotypic profiles according to given correlation structures, and to generate binary phenotypes conditionally to genotypic profiles using a logistic model.

Author(s)

Florian Hébert, Mathieu Emily, David Causeur

Maintainer: Florian Hébert <[e-mail]>

See Also

PopulationSNPSet SampleSNPPhenotype levelplotSNP

Examples

#Simulation of 100,000 genotypic profiles of 10 autocorrelated SNPs,
#with correlation coefficient 0.8. All SNPs have a minor allele frequency equal to 0.4.
X = PopulationSNPSet(100000,Sigma=0.8^abs(outer(1:10,1:10,"-")),maf=rep(0.4,10))

## Not run: 
#Assuming XX is an observed matrix of genotypic profiles, simulation of 100,000
#genotypic profiles according to the correlation matrix and marginal distributions observed in XX:
X = PopulationSNPSet(100000,X=XX)

## End(Not run)

#Generation of a sample of 1,000 cases and 1,000 controls conditionally to the genotypic
#profiles contained in X. The intercept of the logistic model is set to -3, the phenotype
#is associated to SNPs 3 and 7 (parameters 0.2 and 0.4, respectively) under an additive
#and a recessive models, respectively.
tmp = SampleSNPPhenotype(X,-3,c(0.2,0.4),c(3,7),1000,1000,mod=c("A","R"))

#Correlation matrix of the SNPs, estimated on the obtained sample:
levelplotSNP(cor(tmp$SNP))

#Chi-square test between the simulated phenotype and each simulated SNP variable:
apply(tmp$SNP,2,function(x){chisq.test(x,tmp$Phenotype,correct=FALSE)$p.value})

fhebert/SNPSetSimulations documentation built on Jan. 9, 2025, 3:17 a.m.