assocCoxPH: Cox proportional hazards
In amstilp/GWASTools: Tools for Genome Wide Association Studies

Description Usage Arguments Details Value Author(s) See Also Examples

Fits Cox proportional hazards model

assocCoxPH(genoData,
           event,
           time.to.event,
           gene.action = c("additive", "dominant", "recessive"),
           covar = NULL,
           ivar = NULL,
           strata = NULL,
           scan.exclude = NULL,
           effectAllele = c("minor", "alleleA"),
           snpStart = NULL,
           snpEnd = NULL,
           block.size = 5000,
           verbose = TRUE)

`genoData`	a `GenotypeData` object
`event`	name of scan annotation variable in `genoData` for event to analyze (chould be coded 0/1 or FALSE/TRUE)
`time.to.event`	name of scan annotation variable in `genoData` for time to event
`gene.action`	"additive" coding sets the marker variable for homozygous minor allele samples = 2, heterozygous samples = 1, and homozygous major allele samples = 0. "dominant" coding sets the marker variable for homozygous minor allele samples = 2, heterozygous samples = 2, and homozygous major allele samples = 0. "recessive" coding sets the marker variable for homozygous minor allele samples = 2, heterozygous samples = 0, and homozygous major allele samples = 0. (If `effectAllele="alleleA"`, the coding reflects alleleA instead of the minor allele.)
`covar`	a vector of the names of the covariates to adjust for (columns in the scan annotation of `genoData`)
`ivar`	the name of the variable in `covar` to include as an interaction with genotype
`strata`	a vector of names of variables to stratify on for a stratified analysis
`scan.exclude`	a vector of scanIDs for scans to exclude
`effectAllele`	whether the effects should be returned in terms of the minor allele for the tested sample (`effectAllele="minor"`) or the allele returned by `getAlleleA(genoData)` (`effectAllele="alleleA"`). If the minor allele is alleleB for a given SNP, the difference between these two options will be a sign change for the beta estimate.
`snpStart`	index of the first SNP to analyze, defaults to first SNP
`snpEnd`	index of the last SNP to analyze, defaults to last SNP
`block.size`	number of SNPs to read in at once
`verbose`	logical for whether to print status updates

This function performs Cox proportional hazards regression of a survival object (using the Surv function) on SNP genotype and other covariates. It uses the coxph function from the R survival library.

It is recommended to filter results returned using 2*MAF*(1-MAF)*n.events > 75 where MAF = minor allele frequency and n.events = number of events. This filter was suggested by Ken Rice and Thomas Lumley, who found that without this requirement, at threshold levels of significance for genome-wide studies, Cox regression p-values based on standard asymptotic approximations can be notably anti-conservative.

Note: Y chromosome SNPs must be analyzed separately because they only use males.

a data.frame with some or all of the following columns:

`snpID`	the snpIDs
`chr`	chromosome SNPs are on
`n`	number of samples used to analyze each SNP
`n.events`	number of events in complete cases for each SNP
`effect.allele`	which allele ("A" or "B") is the effect allele
`EAF`	effect allele frequency
`MAF`	minor allele frequency
`filter`	`TRUE` if SNP passes the MAF filter (`2MAF(1-MAF)*n.events > 75`)
`Est`	beta estimate for genotype
`SE`	standard error of beta estimate for the genotype
`z.Stat`	z statistic for association
`z.pval`	p-value for association
`GxE.Stat`	Likelihood ratio test statistic for the genotype*ivar interaction parameter
`GxE.pval`	p-value for the likelihood ratio test statistic

Cathy Laurie, Matthew Conomos, Stephanie Gogarten

GenotypeData, coxph

library(GWASdata)
data(illuminaScanADF)
scanAnnot <- illuminaScanADF

# exclude duplicated subjects
scan.exclude <- scanAnnot$scanID[scanAnnot$duplicated]

# create some variables for the scans
scanAnnot$sex <- as.factor(scanAnnot$sex)
scanAnnot$age <- rnorm(nrow(scanAnnot), mean=40, sd=10)
scanAnnot$event <- rbinom(nrow(scanAnnot), 1, 0.4)
scanAnnot$ttoe <- rnorm(nrow(scanAnnot), mean=100, sd=10)

# create data object
gdsfile <- system.file("extdata", "illumina_geno.gds", package="GWASdata")
gds <- GdsGenotypeReader(gdsfile)
genoData <-  GenotypeData(gds, scanAnnot=scanAnnot)

res <- assocCoxPH(genoData,
                  event="event", time.to.event="ttoe",
  		  covar=c("sex", "age"),
  		  scan.exclude=scan.exclude,
  		  snpStart=1, snpEnd=100)

close(genoData)