jointScoreTest {GENESIS} | R Documentation

## Perform a joint score test

### Description

`jointScoreTest` is used to perform a joint score test of a set of variants using a null model and a matrix of genotype dosages.

### Usage

    jointScoreTest(null.model, G)

### Arguments

`null.model` | A null model object returned by `fitNullModel`.
`G` | A matrix of genotype dosages, where samples are the rows and variants are the columns.

### Details

`jointScoreTest` takes the object returned by `fitNullModel` and performs a joint score test for all variants in the `G` matrix using this null model. All effect size and PVE estimates in `fixef` are adjusted for (i.e. conditional on) all other variants included in `G`.

The `G` matrix must be formatted such that the rows are samples and the columns are variants, and the entries are the dosage for that sample and variant. The matrix must have `sample.id` as rownames; this is used to match the genotypes to the null model. Therefore, the same sample identifiers must be used in both `null.model` and `G`. `G` can contain additional samples and ordering is unimportant as long as all samples from the null model are present; it will be subset and reordered to match the set of samples in the null model.

Colnames for `G` are not required. If present, the column names of `G` will be used as the rownames of the `fixef` element and the column and rownames of the `betaCov` element of the output. `fixef` and `betaCov` will be in the same order as the columns in `G`.

Missing data in `G` are not allowed.

### Value

`jointScoreTest` returns a list with the following elements:

`Joint.Stat` | Squared joint score test statistic for all variants in `G`.
`Joint.pval` | p-value associated with the joint score test statistic drawn from a χ^2 distribution with n_\mathrm{variants} degrees of freedom.
`Joint.PVE` | Estimate of the proportion of variance explained jointly by the variants in `G`.
`fixef` | A data.frame with joint effect size estimates (Est), standard errors (SE), chi-squared test statistics (Stat), p-values (pval), and estimated proportion of variance explained (PVE) for each of the variants specified in `G`.
`betaCov` | Estimated covariance matrix for the variants in `G`.

### Author(s)

Adrienne M. Stilp, Matthew P. Conomos

### See Also

`fitNullModel` for fitting the mixed model and performing the variance component estimation. `GenotypeData` and `SeqVarData` for obtaining genotype matrices.

### 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)

    # Prepare genotypes for the first five SNPs and the first 20 samples.
    # Transpose it so that samples are rows and SNPs are columns.
    geno <- t(getGenotype(HapMap_genoData, snp = c(1, 5), scan = c(1, 20)))
    # Set row and column names.
    rownames(geno) <- as.character(GWASTools::getScanID(HapMap_genoData))[1:20]
    colnames(geno) <- sprintf("snp%s", 1:5)

    # Create a phenotype
    set.seed(5)
    phen <- data.frame(
      outcome = rnorm(1:20),
      covar = rnorm(1:20),
      stringsAsFactors = FALSE
    )
    rownames(phen) <- rownames(geno)

    # load saved matrix of KING-robust estimates
    data("HapMap_ASW_MXL_KINGmat")
    cov.mat = HapMap_ASW_MXL_KINGmat[rownames(phen), rownames(phen)]

    # Fit a null model.
    nullmod <- fitNullModel(phen,
                            outcome = "outcome",
                            covars = "covar",
                            cov.mat = cov.mat)

    # Run the joint score test.
    jointScoreTest(nullmod, geno)

    close(HapMap_genoData)

[Package GENESIS version 2.20.1 Index]