Description Usage Arguments Value Author(s) References See Also Examples

It gives p-values of the SPUpath tests and aSPUpath test. Faster than aSPUsPath function when n is large (N > 10^4).

1 2 3 |

`Y` |
Response or phenotype data. It can be a disease indicator; =0 for controls, =1 for cases. Or it can be a quantitative trait. A vector with length n (number of observations). |

`X` |
Genotype or other data; each row for a subject, and each column for an SNP (or a predictor). The value of each SNP is the # of the copies for an allele. A matrix with dimension n by k (n : number of observation, k : number of SNPs (or predictors) ). |

`cov` |
Covariates. A matrix with dimension n by p (n :number of observation, p : number of covariates). |

`model` |
Use "gaussian" for a quantitative trait, and use "binomial" for a binary trait. |

`snp.info` |
SNP information matrix, the 1st column is SNP id, 2nd column is chromosome #, 3rd column indicates SNP location. |

`gene.info` |
GENE information matrix, The 1st column is GENE id, 2nd column is chromosome #, 3rd and 4th column indicate start and end positions of the gene. |

`pow` |
SNP specific power(gamma values) used in SPUpath test. |

`pow2` |
GENE specific power(gamma values) used in SPUpath test. |

`n.perm` |
number of permutations. |

`usePCs` |
indicating whether to extract PCs and then use PCs of X. |

`varprop` |
the proportion of the variations explained (cutoff) that determines how many top PCs to use. |

P-values for SPUpath tests and aSPUpath test.

Il-Youp Kwak and Wei Pan

Wei Pan, Il-Youp Kwak and Peng Wei (2015) A Powerful and Pathway-Based Adaptive Test for Genetic Association With Common or Rare Variants, The American Journal of Human Genetics, 97, 86-98

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## Not run: dat1<-simPathAR1Snp(nGenes=20, nGenes1=5, nSNPlim=c(1, 20),
nSNP0=1, LOR=.2, n=100, MAFlim=c(0.05, 0.4), p0=0.05 )
## End(Not run)
# p-values of SPUpath and aSPUpath tests.
## Not run: p.pathaspu<- aSPUpath2(dat1$Y, dat1$X, snp.info = dat1$snp.info,
gene.info = dat1$gene.info,
model = "binomial", pow=1:8, pow2=c(1, 2, 4, 8), n.perm=1000)
## End(Not run)
p.pathaspu
## pow = 1:8 and pow2 = 1,2,4,8
## So, there are 8*4 = 32 SPUpath p-values.
## SPUpathi,j corresponds pow = i , pow2 = j
## The last element, aSPUpath gives aSPUpath p-value.
``` |

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