varCompCI {GENESIS} | R Documentation

## Variance Component Confidence Intervals

### Description

`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.

### Usage

    varCompCI(nullMMobj, prop = TRUE)

### Arguments

`nullMMobj` | A null model object returned by `fitNullMM`.
`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 `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.

### 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

`fitNullMM` 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(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)

[Package GENESIS version 2.10.1 Index]