aSPU (version 1.50)

aSPUr: Robust Sum of powered score (SPU) tests and aSPU test for a quantitative trait

Description

The test is based on the Huber loss function and using the parametric bootstrap for inference (i.e. bootstrapping residuals).

Usage

aSPUr(Y, X, cov = NULL, pow = c(1:8, Inf), B = 1000, C = 1.345)

Arguments

Y

a vector of quantitative traits (QTs).

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 k2 (n :number of observation, k2 : number of covariates).

pow

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

B

number of bootstraps.

C

Constant in huber loss function. C = 1.345 is chosen to maintain a high efficiency for a Normal error.

Value

p-values of the SPUr tests in the order of supplied pow values; finally, the p-value of the aSPUr test (that combines the SPUs tests with pow by taking their min P-value and adjust for multiple testing).

References

Peng Wei, Ying Cao, Yiwei Zhang, Zhiyuan Xu, Il-Youp Kwak, Eric Boerwinkle, Wei Pan (2016) On Robust Association Testing for Quantitative Traits and Rare Variants, G3, 6(12) 3941-3950.

See Also

aSPU

Examples

# NOT RUN {

data(exdat)

## example analysis using aSPU test on exdat data.

QT <- jitter(exdat$Y)

out <- aSPUr(Y = QT, X = exdat$X, cov = NULL, B = 100)
out
## This is a vector of p-values for SPUr and aSPUr tests.
## SPU1 to SPUInf corresponds with the option pow=c(1:8, Inf)
## They are p-values for corresponding SPUr tests.
## The last element is p-value of aSPUr test.

# }