QRank {QRank}

A new Quantile Rank-score (QRank) based test for the eQTL identification.

Description

A function to obtain the p-value on the association between a gene expression and a genetic variant based on quantile rank-score test.

Usage

QRank(gene, snp, cov = NULL, tau)

Arguments

gene

a gene expression level from a selected gene. No parametric assumption is needed for underlying distribution.

snp

a selected SNP.

cov

a vector or matrix of covariates. Default is NULL.

tau

the quantile levels to be estimated. Tau can be a single value or a vector of quantile levels.

Details

This function conducts Quantile Rank-score (QRank) based test for the continuous traits. It can be used to identify expression quantitative trait loci (eQTLs) that are associated with the conditional quantile functions of gene expression.

Value

composite.pvalue

a single p-value for across all quantile levels under consideration, testing H0: No genetic association at the selected quantile levels.

quantile.specific.pvalue

p-values of each quantile level, testing H_0: The genetic variant and gene expression are not associated at this quantile level.

Author(s)

Xiaoyu Song

References

Xiaoyu Song, Gen Li, Zhenwei Zhou, Xianling Wang, Iuliana Ionita-Laza and Ying Wei (2016). QRank: A Novel Quantile Regression Tool for eQTL Discovery. Under revision for Bioinformatics.

Examples

set.seed(123)                                                                        #
n=300                                                                                #
x=rbinom(n, 2, 0.2)                                                                  #
y=rnorm(n, mean=0, sd=1)                                                             #
z=cbind(rbinom(n, 1, 0.3), rnorm(n, mean=2, sd=2))                                   # 
taus=c( 0.25, 0.5, 0.75)                                                             # 

# - run the proposed QRank approach                                                  #
QRank(gene=y, snp=x, cov=z, tau=taus)                                                #

# - output                                                                           #
#Composite.pvalue:                                                                   #
#[1] 0.2241873                                                                       #

#Quantile.specific.pvalue:                                                           #
#    0.25       0.5      0.75                                                        #
#0.5452044 0.1821452 0.5938421                                                       #