simPathAR1Snp {aSPU} | R Documentation |
It gives a simulated SNPs consisting of multiple genes in a pathway. Each SNPs from a latent multivariate Gaussian variable with an AR1 correlation structure.
simPathAR1Snp( nGenes = 10, nGenes1 = 5, nSNPs = NULL, ncSNPs = NULL, nSNPlim = c(1, 20), nSNP0 = 1:3, LOR = 0.3, n = 100, MAFlim = c(0.05, 0.4), rholim = c(0, 0), p0 = 0.05, noncausal = FALSE )
nGenes |
The number of total genes. |
nGenes1 |
The number of causal genes. |
nSNPs |
A vector, length matched with total number of genes. Each elements of vector indicate the number of SNPs in the gene. Default is nSNPs = NULL, in this case the number of nSNPs randomly selected from nSNPlow to nSNPup. |
ncSNPs |
A vector, length matched with total number of genes. Each elements of vector indicate the number of causal SNPs in the gene. Default is ncSNPs = NULL, in this case the number of ncSNPs are randomly selected from nSNP0. |
nSNPlim |
If nSNPs = NULL, the number of SNPs in Gene randomly selected from the ranges in nSNPlim. |
nSNP0 |
If ncSNPs = NULL, the number of causal SNPs in Gene randomly selected from nSNP0. Default is 1:3. |
LOR |
Association in log OR between a causal SNP and outcome. |
n |
# of cases (= # of controls). |
MAFlim |
MAF's of the SNPs are drawn from Unif(MAFlim[1], MAFlim[2]). |
rholim |
the SNPs in eahc gene are from a latent Normal variable with a AR(rho) corr structure, rho's are drawn from Unif(rholim[1], rholim[2]); the SNPs in diff genes are independant. |
p0 |
background disease prevalence;i.e. intercept=log(p0/(1-p0)). |
noncausal |
exclude causal SNPs if TRUE, it is the simulation set up d in the paper(Pan et al 2015). |
a list of the binary outcome Y (=0 or 1) and SNPs (=0, 1 or 2); Y is a vector of length 2n; X is a matrix of 2n by nSNP.
# Simulation set up A a) in the paper (Pan et al 2015) ## Not run: simula <- simPathAR1Snp(nGenes=20, nGenes1=1, nSNPlim=c(1, 20), nSNP0=1:3, LOR=.2, rholim=c(0,0), n=100, MAFlim=c(0.05, 0.4), p0=0.05) ## End(Not run) # Simulation set up A b) in the paper #simulb <- simPathAR1Snp(nGenes=20, nGenes1=1, nSNPlim=c(1, 100), nSNP0=1:3, # LOR=.2, rholim=c(0,0), # n=100, MAFlim=c(0.05, 0.4), p0=0.05)