geneStat: Compute Gene-Level Statistics Based on Efficient Score...

In groupedSurv: Efficient Estimation of Grouped Survival Models Using the Exact Likelihood Function

Compute Gene-Level Statistics Based on Efficient Score Statistics for the Grouped Survival Model

Description

A method to compute gene-level statistics using the contributions of individual samples to the efficient score statistics of SNPs under a grouped survival model. The function supports zero, single, or multiple covariates as dictated by the input data. The function can take either a gwaa.data object or a standard data.frame or matrix as input. The contribution of each sample to the efficient score will be computed separately for each SNP being tested, and then gene-level statistics will be computed using either the default Sequence Kernel Association Test (SKAT) or a user-provided function.

Usage

	geneStat(x, Z=NULL, GenABEL.data=NULL, alpha, theta=NULL, gtime, delta,
           beta=0, nCores=1, 
           FUN=function(Uij,weight){sum((colSums(Uij)*weight)^2)}, geneSet)

Arguments

x	Vector, data.frame, or matrix of numeric variables of interest for each sample. Alternatively, if a gwaa.data object is given for GenABEL.data, this argument may be a vector of strings corresponding to column names in GenABEL.data@gtdata to use as variables of interest.
Z	Optional data.frame or matrix of numeric covariate values for each sample. Alternatively, if a gwaa.data object is given for GenABEL.data, this argument may be a vector of strings corresponding to column names in GenABEL.data@phdata to use as covariates.
GenABEL.data	Optional gwaa.data object.
alpha	Vector of baseline survival rates for each time interval.
theta	Optional vector of estimated nuisance parameters for the covariates.
gtime	Vector of observed survival times for each sample.
delta	Vector of event indicators for each sample: 1 indicates observed event, 0 indicates censored.
beta	Scalar for the parameter of interest. Default is 0.
nCores	Integer representing the number of cores to be used for multi-threaded computation. Default is 1.
FUN	A function to compute the gene-level statistics base on the SNP-level efficient scores. The function arguments should include a matrix of each samples' contribution to the efficient scores, Uij, and a vector of weights, weight. Default is the SKAT statistic.
geneSet	A list of lists, where each sub-list contains two vectors: a vector of column names of the variables of interest to be included in that set, and a vector of numerical weights corresponding to each specified variable of interest.

Value

list with two elements: stat, a list of return objects from the default or user-defined FUN, and U, a list of matrices of the contribution of each subject to the efficient score statistics for each SNP in each gene set. Rows correspond to samples, and columns correspond to the variables of interest.

References

Wu, M.C., Lee, S., Cai, T., Li, Y., Boehnke, M. and Lin, X. (2011). Rare-variant association testing for sequencing data with the sequence kernel association test. American Journal of Human Genetics, 89:1, 82-93.
Ionita-Laza, I., Lee, S., Makarov, V., Buxbaum, J.D., and Lin, X. (2013). Sequence kernel association tests for the combined effect of rare and common variants. American Journal of Human Genetics, 92:6, 841-53.

Examples

# Generate dummy data
rm(list=ls())
set.seed(111)
n <- 1000

# covariate parameters
theta <- c(0.2, 0.2)

# covariate data (centered at 0)
z1 <- rnorm(n)
z2 <- rbinom(n, 1, 0.5) - 0.5
Z <- matrix(cbind(z1, z2), ncol = 2)

# continuous survival time
lam0 <- 1
cmax <- 3
lami <- lam0 * exp(Z[,1]*theta[1]+Z[,2]*theta[2])
stime <- rexp(n, lami)
ctime <- runif(n, 0, cmax)
delta <- stime < ctime
otime <- pmin(stime, ctime)

# number of observation times
ntps <- 5

# number of intervals
r <- ntps + 1

# last observation time
maxbreakq <- 0.85
maxbreak <- qexp(maxbreakq, lam0)

# grouped survival times
breaks <- (1:ntps) * (maxbreak/ntps)
gtime <- findInterval(otime, breaks) + 1
delta[gtime == r] <- FALSE
dctime <- findInterval(ctime, breaks) + 1
delta[gtime == dctime] <- FALSE
delta <- as.numeric(delta)
gtime[which(gtime == r)] <- Inf

# estimate nuisance parameters
#thetaest <- thetaEst(Z, gtime, delta)

# SNP and gene information
#geneInfo <- data.frame(gene=c("BRCA1","BRCA2"), chr=c(17,13),
#                       start=c(41196312, 32889611), end=c(41277500, 32973805),
#                       stringsAsFactors=FALSE)
#snpInfo <- data.frame(chr=c(17,17,13,13),
#                      pos=c(41211653,41213996,32890026,32890572),
#                      rsid=c("rs8176273","rs8176265","rs9562605","rs1799943"),
#                      stringsAsFactors=FALSE)

# use snplist package to create gene sets
#library(snplist)
#setGeneTable(geneInfo)
#setSNPTable(snpInfo)
#geneset <- makeGeneSet()

# simulate sample values for SNPs
#G <- matrix(rbinom(n*nrow(snpInfo), 2, 0.5), ncol=nrow(snpInfo))
#colnames(G) <- snpInfo$rsid

# dummy weights for the SNPs in the gene sets
#for(i in seq_len(length(geneset))){
#    weight <- rep(1, length(geneset[[i]]))
#    geneset[[i]] <- list(geneset[[i]], weight)

#}

# compute gene-level statistics based on the default SKAT function
#res <- geneStat(x=G, Z=Z, alpha=thetaest$alpha, theta=thetaest$theta,
#                gtime=gtime, delta=delta, geneSet=geneset, beta=0, nCores=1)
#res$stat

groupedSurv documentation built on Sept. 29, 2023, 1:06 a.m.