| GEEaSPUset {GEEaSPU} | R Documentation |
An adaptive sum of powered score (SPU) test for SNP-set (gene) and mutiple phenotypes
Description
Tests whether SNP-set (gene) is associated with multivariate outcomes; provides a series of p-values of GEE-SPU(pow, pow2) and GEEaSPUset tests.
Usage
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)
Arguments
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. |
Details
Adaptive association tests for SNP-set (gene) and multiple phenotypes using GEE.
Value
a vector of p-values from GEE-SPU(pow, pow2) tests and GEE-aSPUset test.
Note
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".
Author(s)
Junghi Kim and Wei Pan
References
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.
Examples
# -- 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