varCompCI {GENESIS} | 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

### See Also

`fitNullModel` for fitting the mixed model and performing the variance component estimation.

### 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)
    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)

[Package GENESIS version 2.22.2 Index]