assocCoxPH: Cox proportional hazards
In 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,
           cluster = NULL,
           scan.exclude = NULL,
           LRtest = FALSE,
           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
`cluster`	the name of a column (in the scan annotation of `genoData`) which clusters the observations for robust variance. See `cluster`

`scan.exclude`	a vector of scanIDs for scans to exclude
`LRtest`	logical for whether to perform Likelihood Ratio Tests in addition to Wald tests (which are always performed).
`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
`maf.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
`Wald.Stat`	chi-squared test statistic for association
`Wald.pval`	p-value for association
`LR.Stat`	likelihood ratio test statistic for association
`LR.pval`	p-value for association
`GxE.Est`	beta estimate for the genotype*ivar interaction parameter (`NA` if this parameter is a factor with >2 levels)
`GxE.SE`	standard error of beta estimate for the genotype*ivar interaction parameter
`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, David Levine

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)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Reading in Phenotype and Covariate Data...
Running analysis with 43 Samples
Beginning Calculations...
Block 1 of 1 Completed - 0.5859 secs
Warning messages:
1: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3 ; coefficient may be infinite. 
2: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3 ; coefficient may be infinite. 
3: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3 ; coefficient may be infinite. 
4: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3 ; coefficient may be infinite. 
5: In fitter(X, Y, istrat, offset, init, control, weights = weights,  :
  Loglik converged before variable  3 ; coefficient may be infinite.