aSPU {aSPU}

Sum of Powered Score (SPU) tests and adaptive SPU (aSPU) test for single trait - SNP set association.

Description

It gives p-values of the SPU tests and aSPU test.

Usage

aSPU(
  Y,
  X,
  cov = NULL,
  resample = c("perm", "boot", "sim"),
  model = c("gaussian", "binomial"),
  pow = c(1:8, Inf),
  n.perm = 1000,
  threshold = 10^5
)

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).

resample

Use "perm" for residual permutations, "sim" for simulations from the null distribution, and "boot" for parametric bootstrap.

model

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

pow

power used in SPU test. A vector of the powers.

n.perm

number of permutations or bootstraps.

threshold

When n.perm is less than the threshold, we use faster version of aSPU but use more memory. (default is 10^5)

Value

A list object, Ts : test statistics for the SPU tests (in the order of the specified pow) and finally for the aSPU test. pvs : p-values for the SPU and aSPU tests.

Author(s)

Il-Youp Kwak, Junghi Kim, Yiwei Zhang and Wei Pan

References

Wei Pan, Junghi Kim, Yiwei Zhang, Xiaotong Shen and Peng Wei (2014) A powerful and adaptive association test for rare variants, Genetics, 197(4), 1081-95

Junghi Kim, Jeffrey R Wozniak, Bryon A Mueller, Xiaotong Shen and Wei Pan (2014) Comparison of statistical tests for group differences in brain functional networks, NeuroImage, 1;101:681-694

See Also

aSPUw

Examples

data(exdat)

## example analysis using aSPU test on exdat data.
# increase n.perm for better p.val 
## Not run: out <- aSPU(exdat$Y, exdat$X, cov = NULL, resample = "boot",
            model = "binomial", pow = c(1:8, Inf), n.perm = 1000) 
## End(Not run)


out$Ts
# This is a vector of Test Statistics for SPU and aSPU tests.
# SPU1 to SPUInf corresponds with the option pow=c(1:8, Inf)
# They are SPU test statistics.
# The last element aSPU is minimum of them, aSPU statistic.

out$pvs
# This is a vector of p-values for SPU and aSPU tests.
# SPU1 to SPUInf corresponds with the option pow=c(1:8, Inf)
# They are p-values for corresponding SPU tests.
# The last element is p-value of aSPU test.