or relatedness

varCompCI

R Documentation

Variance Component Confidence Intervals

Description

varCompCI provides confidence intervals for the variance component estimates found using fitNullModel. The confidence intervals can be found on either the original scale or for the proportion of total variability explained.

Usage

varCompCI(null.model, prop = TRUE)

Arguments

`null.model`	A null model object returned by `fitNullModel`.
`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).

Details

varCompCI takes the object returned by fitNullModel 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 fitNullModel, then this function can be used to provide a heritability estimate when prop is TRUE.

Value

varCompCI prints a table of point estimates and 95% confidence interval limits for each estimated variance component.

Author(s)

Matthew P. Conomos

Examples

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(HapMap_genoData, kinobj = HapMap_ASW_MXL_KINGmat, 
                 divobj = HapMap_ASW_MXL_KINGmat)

# run PC-Relate
HapMap_genoData <- GenotypeBlockIterator(HapMap_genoData, snpBlock=20000)
mypcrel <- pcrelate(HapMap_genoData, pcs = mypcair$vectors[,1,drop=FALSE],
    			training.set = mypcair$unrels,
                        BPPARAM = BiocParallel::SerialParam())
close(HapMap_genoData)

# generate a phenotype
set.seed(4)
pheno <- 0.2*mypcair$vectors[,1] + rnorm(mypcair$nsamp, mean = 0, sd = 1)

annot <- data.frame(sample.id = mypcair$sample.id, 
                    pc1 = mypcair$vectors[,1], pheno = pheno)

# make covariance matrix
cov.mat <- pcrelateToMatrix(mypcrel, verbose=FALSE)[annot$sample.id, annot$sample.id]

# fit the null mixed model
nullmod <- fitNullModel(annot, outcome = "pheno", covars = "pc1", cov.mat = cov.mat)

# find the variance component CIs
varCompCI(nullmod, prop = TRUE)
varCompCI(nullmod, prop = FALSE)

UW-GAC/GENESIS documentation built on May 16, 2024, 1:10 p.m.