GEEaSPUset: An adaptive sum of powered score (SPU) test for SNP-set...
In GEEaSPU: Adaptive Association Tests for Multiple Phenotypes using Generalized Estimating Equations (GEE)

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/GEEaSPU.R

Tests whether SNP-set (gene) is associated with multivariate outcomes; provides a series of p-values of GEE-SPU(pow, pow2) and GEEaSPUset tests.

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GEEaSPUset(pheno, geno, Z = NULL, model = "gaussian", corstr = "independence", 
          pow = c(1,2,4,8), pow2 = c(1,2,4,8), n.perm = 1000, 
          null.type = "perm", score.test = FALSE)

`pheno`	a numeric phenotype matrix with each row as a different individual and each column as a separate phenotype.
`geno`	a numeric genotype matrix with each row as a different individual and each column as a snp.
`Z`	a numeric covariate matrix with each row as a different individual and each column as a covariated to be adjested.
`model`	a character string specifying the model of the phenotypes. Models supported are "gaussian" for a quantitative trait and "binomial" for a binary trait (default = "gaussian").
`corstr`	a character string specifying the correlation structure of phenotypes. The following are permitted: "independence", "fixed", "stat_M_dep", "non_stat_M_dep", "exchangeable", "AR-M" and "unstructured" (default = "independence").
`pow`	a vector of the power weight to be used at a SNP level (default = c(1,2,4,8)).
`pow2`	a vector of the power weight to be used at a trait level (default = c(1,2,4,8)).
`n.perm`	a numeric value of number of null statistics (default = 1000).
`null.type`	a character string specifying how to generate null statistics; "perm" is used when null statistics are generated using permutations and "sim" is used when null statistics are generated using simulations (default = "perm").
`score.test`	a logical value indicating whether to include GEEaSPU-Score test along with GEE-Score test (default = FALSE). If TRUE, it computes p-values of GEEaSPU-Score and GEE-Score as well as GEEaSPU test.

Adaptive association tests for SNP-set (gene) and multiple phenotypes using GEE.

a vector of p-values from GEE-SPU(pow, pow2) tests and GEE-aSPUset test.

When large SNP-set (namely large gene size) or large number of phenotypes are included, the permuation based test (null.type = "perm") is recommended.

An option "binomial" model only supports the option, null.type="sim".

Junghi Kim and Wei Pan

Kim J, Zhang Y, and Pan W (2016). "Powerful and adaptive testing for multi-trait and multi-SNP associations with GWAS and sequencing data." Genetics, 203(2), 715–731.

# -- simulating phenotypes
# -- n.subjects: number of subjects
# -- n.traits: number of phenotypes
# -- Sigma: covariance matrix of phenotypes (e.g. AR(1))

set.seed(136)
n.subjects <- 100
n.traits <- 3
sigma <- 2; rho <- 0.5
Sigma0 <- diag(n.traits); 
Sigma <- sigma * rho^abs(row(Sigma0) - col(Sigma0))
eS <- eigen(Sigma, symmetric = TRUE)
ev <- eS$values
X <- matrix(rnorm(n.subjects * n.traits), n.subjects)
pheno <- X %*% diag(sqrt(pmax(ev, 0)), ncol(Sigma)) %*% eS$vectors


# -- simulating genotype
# -- n.geno: number of SNPs included in the SNP set/gene

n.geno <- 3
maf <- 0.2
geno <- matrix(rbinom(n = n.subjects * n.geno, size = 2, prob = maf), ncol = n.geno)


# -- Computing the p-value of GEEaSPUpath test with the permutation based method
Pvl <- GEEaSPUset(pheno = pheno, geno = geno, Z = NULL, model = "gaussian", 
        corstr = "independence", pow = c(1,4,Inf), pow2 = c(1,4,Inf), n.perm = 1000,         
        null.type = "perm", score.test = FALSE)


# -- Each element of Pvl is a p value of GEE-SPU(pow,pow2) in order
# -- The last element of Pvl is a p value of GEE-aSPUset test
Pvl
Pvl[length(Pvl)]


# > Pvl
#     SPU.1.1     SPU.1.4   SPU.1.Inf     SPU.4.1     SPU.4.4   SPU.4.Inf   SPU.Inf.1 
#  0.01400000  0.08800000  0.07200000  0.53000000  0.41000000  0.32100000  0.55100000 
#   SPU.Inf.4 SPU.Inf.Inf     aSPUset 
#  0.48700000  0.41000000  0.04095904 
 
 
# > Pvl[length(Pvl)]
#    aSPUset 
# 0.04095904