CoxKM: KM for Survival Traits in GWAS Data (calculate p-value)

In KMgene: Gene-Based Association Analysis for Complex Traits

Description

This function (CoxKM) is used to perform the KM analysis (Chen et al., 2014) for survival traits in GWAS data

Usage

CoxKM(
  object,
  SNPInfo = NULL,
  wts = function(maf) {     dbeta(maf, 1, 25) },
  method = "saddlepoint",
  snpNames = "Name",
  aggregateBy = "gene",
  mafRange = c(0, 0.5),
  verbose = FALSE
)

Arguments

object	Object saved from prepCoxKM.
SNPInfo	The SNP Info file. This should contain the fields listed in snpNames and aggregateBy. Only SNPs in this table will be meta analyzed, so this may be used to restrict the analysis.
wts	Either a function to calculate testing weights, or a character specifying a vector of weights in the SNPInfo file. For skatMeta the default are the ‘beta’ weights.
method	The p-value calculation method. Default is "saddlepoint", "integration" is the Davies method used in the SKAT package.
snpNames	The field of SNPInfo where the SNP identifiers are found. Default is "Name"
aggregateBy	The field of SNPInfo on which the skat results were aggregated. Default is "gene".
mafRange	Range of MAF’s to include in the analysis (endpoints included). Default is all SNPs (0 <= MAF <= 0.5).
verbose	Logical. Whether progress bars should be printed.

Value

output: survival trait KM (CoxKM) p-value

Examples

##########################################################
### Examples for Survival Traits in GWAS Data using KM ###
##########################################################
data("CoxKM_data")
library(seqMeta)
cov <- as.matrix(pheno1[,2:3])
p<-vector()
for (i in 1:2) {
  Gene <- unique(SNPInfo$gene)[i]
  choose <- SNPInfo$gene == Gene
  geno <- Z1[,choose]
  cohort1 <- prepCoxKM(Z=Z1, Surv(time, status)~strata(sex)+bmi, 
  SNPInfo=SNPInfo[choose,], data=pheno1)
  p[i] <- CoxKM(cohort1, SNPInfo=SNPInfo[choose,])
}

KMgene documentation built on July 8, 2020, 6:09 p.m.