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 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)
```

[Package *aSPU* version 1.50 Index]