GEEaSPUpath: An adaptive sum of powered score (SPU) test for gene-set...
In GEEaSPU: Adaptive Association Tests for Multiple Phenotypes using Generalized Estimating Equations (GEE)
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
Examples
Description
Tests whether gene-set (pathway) is associated with multivariate outcomes;
provides a series of p-values of GEE-SPU(pow, pow2, pow3) and GEEaSPUpath tests.
Usage
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2
GEEaSPUpath(pheno, geno, nSNPs, Z = NULL, corstr = "independence",
pow = c(1,2,4,8), pow2 = c(1,2,4,8), pow3 = c(1,2,4,8), n.perm = 1000)
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; the SNPs (with the number stored in nSNPs) from one gene are
stored consecutively from the first gene.
nSNPs
A numeric vector, whose length matches to the total number of genes;
each element of vector indicate the number of SNPs in each gene.
Z
a numeric covariate matrix with each row as a different individual and each column as a covariated to be adjested.
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)).
pow3
a vector of the power weight to be used at a gene level (default = c(1,2,4,8)).
n.perm
a numeric value of number of null statistics (default = 1000).
Details
Adaptive association tests for gene-set (pathway) and multiple phenotypes using GEE.
Value
a vector of p-values from GEE-SPU(pow, pow2, pow3) tests and GEE-aSPUpath test.
Note
GEEaSPUpath function only supports a case for a quantitative trait (model = "gaussian") and a permutation based test (null.type = "perm").
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
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# -- 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
# -- Assume we have two genes each of which has 3 and 5 SNPs respectively.
# -- n.geno1: number of SNPs included in the gene1
# -- n.geno2: number of SNPs included in the gene2
# -- nSNPs <- c(3,5)
n.geno1 <- 3
n.geno2 <- 5
maf1 <- 0.2
maf2 <- 0.4
gene1 <- matrix(rbinom(n = n.subjects*n.geno1, size = 2, prob = maf1), ncol = n.geno1)
gene2 <- matrix(rbinom(n = n.subjects*n.geno2, size = 2, prob = maf2), ncol = n.geno2)
geno <- cbind(gene1, gene2)
# -- Computing the p-value of GEEaSPUpath test
Pvl <- GEEaSPUpath(pheno = pheno, geno = geno, nSNPs = c(3,5), Z = NULL,
corstr = "independence", pow = c(1,4,8), pow2 = c(1,4,8),
pow3 = c(1,4,8), n.perm = 1000)
# -- Each element of Pvl is a p value of GEE-SPU(pow,pow2,pow3) in order
# -- The last element of Pvl is a p value of GEE-aSPUpath test
Pvl
Pvl[length(Pvl)]
# > Pvl
# SPU.1.1.1 SPU.1.1.4 SPU.1.1.8 SPU.1.4.1 SPU.1.4.4 SPU.1.4.8 SPU.1.8.1 SPU.1.8.4
# 0.00900000 0.05600000 0.07000000 0.06200000 0.08300000 0.11200000 0.06100000 0.08200000
# SPU.1.8.8 SPU.4.1.1 SPU.4.1.4 SPU.4.1.8 SPU.4.4.1 SPU.4.4.4 SPU.4.4.8 SPU.4.8.1
# 0.10600000 0.58100000 0.54300000 0.49200000 0.62400000 0.64000000 0.62700000 0.64900000
# SPU.4.8.4 SPU.4.8.8 SPU.8.1.1 SPU.8.1.4 SPU.8.1.8 SPU.8.4.1 SPU.8.4.4 SPU.8.4.8
# 0.67100000 0.67500000 0.58300000 0.53700000 0.48100000 0.63400000 0.64600000 0.63800000
# SPU.8.8.1 SPU.8.8.4 SPU.8.8.8 aSPUpath
# 0.66000000 0.68100000 0.67900000 0.04395604
# > Pvl[length(Pvl)]
# aSPUpath
# 0.04395604
GEEaSPU documentation built on May 2, 2019, 2:33 a.m.
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Description Usage Arguments Details Value Note Author(s) References Examples
Description
Tests whether gene-set (pathway) is associated with multivariate outcomes; provides a series of p-values of GEE-SPU(pow, pow2, pow3) and GEEaSPUpath tests.
Usage
1 2 | GEEaSPUpath(pheno, geno, nSNPs, Z = NULL, corstr = "independence",
pow = c(1,2,4,8), pow2 = c(1,2,4,8), pow3 = c(1,2,4,8), n.perm = 1000)
|
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; the SNPs (with the number stored in nSNPs) from one gene are stored consecutively from the first gene. |
nSNPs |
A numeric vector, whose length matches to the total number of genes; each element of vector indicate the number of SNPs in each gene. |
Z |
a numeric covariate matrix with each row as a different individual and each column as a covariated to be adjested. |
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)). |
pow3 |
a vector of the power weight to be used at a gene level (default = c(1,2,4,8)). |
n.perm |
a numeric value of number of null statistics (default = 1000). |
Details
Adaptive association tests for gene-set (pathway) and multiple phenotypes using GEE.
Value
a vector of p-values from GEE-SPU(pow, pow2, pow3) tests and GEE-aSPUpath test.
Note
GEEaSPUpath function only supports a case for a quantitative trait (model = "gaussian") and a permutation based test (null.type = "perm").
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | # -- 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
# -- Assume we have two genes each of which has 3 and 5 SNPs respectively.
# -- n.geno1: number of SNPs included in the gene1
# -- n.geno2: number of SNPs included in the gene2
# -- nSNPs <- c(3,5)
n.geno1 <- 3
n.geno2 <- 5
maf1 <- 0.2
maf2 <- 0.4
gene1 <- matrix(rbinom(n = n.subjects*n.geno1, size = 2, prob = maf1), ncol = n.geno1)
gene2 <- matrix(rbinom(n = n.subjects*n.geno2, size = 2, prob = maf2), ncol = n.geno2)
geno <- cbind(gene1, gene2)
# -- Computing the p-value of GEEaSPUpath test
Pvl <- GEEaSPUpath(pheno = pheno, geno = geno, nSNPs = c(3,5), Z = NULL,
corstr = "independence", pow = c(1,4,8), pow2 = c(1,4,8),
pow3 = c(1,4,8), n.perm = 1000)
# -- Each element of Pvl is a p value of GEE-SPU(pow,pow2,pow3) in order
# -- The last element of Pvl is a p value of GEE-aSPUpath test
Pvl
Pvl[length(Pvl)]
# > Pvl
# SPU.1.1.1 SPU.1.1.4 SPU.1.1.8 SPU.1.4.1 SPU.1.4.4 SPU.1.4.8 SPU.1.8.1 SPU.1.8.4
# 0.00900000 0.05600000 0.07000000 0.06200000 0.08300000 0.11200000 0.06100000 0.08200000
# SPU.1.8.8 SPU.4.1.1 SPU.4.1.4 SPU.4.1.8 SPU.4.4.1 SPU.4.4.4 SPU.4.4.8 SPU.4.8.1
# 0.10600000 0.58100000 0.54300000 0.49200000 0.62400000 0.64000000 0.62700000 0.64900000
# SPU.4.8.4 SPU.4.8.8 SPU.8.1.1 SPU.8.1.4 SPU.8.1.8 SPU.8.4.1 SPU.8.4.4 SPU.8.4.8
# 0.67100000 0.67500000 0.58300000 0.53700000 0.48100000 0.63400000 0.64600000 0.63800000
# SPU.8.8.1 SPU.8.8.4 SPU.8.8.8 aSPUpath
# 0.66000000 0.68100000 0.67900000 0.04395604
# > Pvl[length(Pvl)]
# aSPUpath
# 0.04395604
|
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