aSPUpath2 {aSPU} | R Documentation |
It gives p-values of the SPUpath tests and aSPUpath test. Faster than aSPUsPath function when n is large (N > 10^4).
aSPUpath2( Y, X, cov = NULL, model = c("binomial", "gaussian"), snp.info, gene.info, pow = c(1:8, Inf), pow2 = c(1, 2, 4, 8), n.perm = 200, usePCs = F, varprop = 0.95 )
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
## 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.