varCompCI {GENESIS} | R Documentation |
varCompCI
provides confidence intervals for the variance component estimates found using fitNullMM
. The confidence intervals can be found on either the original scale or for the proportion of total variability explained.
varCompCI(nullMMobj, prop = TRUE)
nullMMobj |
A null model object returned by |
prop |
A logical indicator of whether the point estimates and confidence intervals should be returned as the proportion of total variability explained (TRUE) or on the orginal scale (FALSE). |
varCompCI
takes the object returned by fitNullMM
as its input and returns point estimates and confidence intervals for each of the random effects variance component estimates. If a kinship matrix or genetic relationship matrix (GRM) was included as a random effect in the model fit using fitNullMM
, then this function can be used to provide a heritability estimate when prop
is TRUE.
varCompCI
prints a table of point estimates and 95% confidence interval limits for each estimated variance component.
Matthew P. Conomos
fitNullMM
for fitting the mixed model and performing the variance component estimation.
library(GWASTools) # file path to GDS file gdsfile <- system.file("extdata", "HapMap_ASW_MXL_geno.gds", package="GENESIS") # read in GDS data HapMap_geno <- GdsGenotypeReader(filename = gdsfile) # create a GenotypeData class object HapMap_genoData <- GenotypeData(HapMap_geno) # load saved matrix of KING-robust estimates data("HapMap_ASW_MXL_KINGmat") # run PC-AiR mypcair <- pcair(genoData = HapMap_genoData, kinMat = HapMap_ASW_MXL_KINGmat, divMat = HapMap_ASW_MXL_KINGmat) # run PC-Relate mypcrel <- pcrelate(genoData = HapMap_genoData, pcMat = mypcair$vectors[,1], training.set = mypcair$unrels) close(HapMap_genoData) # generate a phenotype set.seed(4) pheno <- 0.2*mypcair$vectors[,1] + rnorm(mypcair$nsamp, mean = 0, sd = 1) # make ScanAnnotationDataFrame scanAnnot <- ScanAnnotationDataFrame(data.frame(scanID = mypcrel$sample.id, pc1 = mypcair$vectors[,1], pheno = pheno)) # make covMatList covMatList <- list("Kin" = pcrelateMakeGRM(mypcrel)) # fit the null mixed model nullmod <- fitNullMM(scanData = scanAnnot, outcome = "pheno", covars = "pc1", covMatList = covMatList) # find the variance component CIs varCompCI(nullmod, prop = TRUE) varCompCI(nullmod, prop = FALSE)