aSPU (version 1.50)

aSPUpath: Pathway based Sum of Powered Score tests (SPUpath) and adaptive SPUpath (aSPUpath) test for single trait - pathway association.

Description

It gives p-values of the SPUpath tests and aSPUpath test.

Usage

aSPUpath(
  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
)

Arguments

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.

Value

P-values for SPUpath tests and aSPUpath test.

References

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

See Also

simPathAR1Snp

Examples

# NOT RUN {
# }
# 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 ) 
# }
# NOT RUN {
# p-values of SPUpath and aSPUpath tests.
# }
# NOT RUN {
p.pathaspu<- aSPUpath(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) 
# }
# 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.

# }