assocTest {podkat} | R Documentation

## Perform Association Test

### Description

Method for performing a kernel-based association test given a genotype, VCF file, or kernel matrix

### Usage

    ## S4 method for signature 'GenotypeMatrix,NullModel'
    assocTest(Z, model, ranges,
              kernel=c("linear.podkat", "localsim.podkat",
                       "quadratic.podkat", "linear.SKAT",
                       "localsim.SKAT", "quadratic.SKAT"),
              width=1000, weights=NULL, weightFunc=betaWeights(),
              method=NULL, adj=c("automatic", "none", "force"),
              pValueLimit=0.05)
    ## S4 method for signature 'matrix,NullModel'
    assocTest(Z, model, method=NULL,
              adj=c("automatic", "none", "force"), pValueLimit=0.05)
    ## S4 method for signature 'TabixFile,NullModel'
    assocTest(Z, model, ranges,
              kernel=c("linear.podkat", "localsim.podkat",
                       "quadratic.podkat", "linear.SKAT",
                       "localsim.SKAT", "quadratic.SKAT"),
              cl=NULL, nnodes=1, batchSize=20,
              noIndels=TRUE, onlyPass=TRUE, na.limit=1, MAF.limit=1,
              na.action=c("impute.major", "omit"),
              MAF.action=c("invert", "omit", "ignore"),
              sex=NULL, weightFunc=betaWeights(), width=1000,
              method=NULL, adj=c("automatic", "none", "force"),
              pValueLimit=(0.1 / length(ranges)), tmpdir=tempdir(),
              displayProgress=TRUE)
    ## S4 method for signature 'character,NullModel'
    assocTest(Z, model, ...)

### Arguments

`Z` | an object of class `GenotypeMatrix`, a quadratic kernel matrix, an object of class `TabixFile`, or a character string with a file name
`model` | an object of class `NullModel`
`ranges` | an object with genomic regions to be tested; may be an object of class `GRanges` or `GRangesList`. If missing, `assocTest` takes the whole genotype matrix or the genotypes in the VCF file as a whole.
`kernel` | determines the kernel that should be used for association testing (see Subsection 9.2 of the package vignette for details)
`width` | tolerance radius parameter for position-dependent kernels “linear.podkat”, “quadratic.podkat”, and “localsim.podkat”; must be single positive numeric value; ignored for kernels “linear.SKAT”, “quadratic.SKAT”, and “localsim.SKAT” (see Subsection 9.2 of the package vignette for details)
`weights` | for the method with signature `GenotypeMatrix,NullModel`, it is also possible to supply weights directly as a numeric vector that is as long as the number of columns of `Z`. In this case, the argument `weightFunc` is ignored. Use `NULL` (default) to use automatic weighting with the function supplied as argument `weightFunc`. If `weightFunc` is `NULL` too, no weighting takes place, i.e. an unweighted kernel is used.
`weightFunc` | function for computing variant weights from minor allele frequencies (MAFs); see `weightFuncs` for weighting and Subsection 9.3 of the package vignette for functions provided by the podkat package. Use `NULL` for unweighted kernels.
`method` | identifies the method for computing the p-values. If the null model is of type “logistic” and small sample correction is applied (see argument `adj` below), possible values are “unbiased”, “population”, “sample”, and “SKAT” (see details below and Subsection 9.5 of the package vignette). If the null model is of type “linear” or if the null model is of type “logistic” and no small sample correction is applied, possible values are “davies”, “liu”, and “liu.mod” (see details below and Subsection 9.1 of the package vignette). If the null model is of type “bernoulli”, this argument is ignored.
`adj` | whether or not to use small sample correction for logistic models (binary trait with covariates). The choice “none” turns off small sample correction. If “force” is chosen, small sample correction is turned on unconditionally. If “automatic” is chosen (default), small sample correction is turned on if the number of samples does not exceed 2,000. This argument is ignored for any type of model except “logistic” and small sample correction is switched off. For details how to train a null model for small sample correction, see `nullModel` and Sections 4 and 9.5 of the package vignette. An adjustment of higher moments is performed whenever sampled null model residuals are available in the null model `model` (slot `res.resampled.adj`, see `NullModel`).
`pValueLimit` | if the null model is of type “bernoulli”, `assocTest` performs an exact mixture of Bernoulli test. This test uses a combinatorial algorithm to compute exact p-values and, for the sake of computational efficiency, quits if a pre-specified p-value threshold is exceeded. This threshold can be specified with the `pValueLimit` argument. This argument is ignored for other types of tests/null models.
`cl` | if `cl` is an object of class `SOCKcluster`, association testing is carried out in parallel on the cluster specified by `cl`. If `NULL` (default), either no parallelization is done (if `nnodes=1`) or `assocTest` launches a cluster with `nnodes` R client processes on `localhost`. See Subsection 8.5.2 of the package vignette.
`nnodes` | if `cl` is `NULL` and `nnodes` is greater than 1, `makePSOCKcluster` is called with `nnodes` nodes on `localhost`, i.e. `nnodes` R slave processes are launched on which association testing is carried out in parallel. The default is `1`. See Subsection 8.5.2 of the package vignette.
`batchSize` | parameter which determines how many regions of `ranges` are processed at once. The larger `batchSize`, the larger the the batches that are read from the VCF file `Z`. A larger `batchSize` reduces the number of individual read operations, which improves performance. However, a larger `batchSize` also requires larger amounts of memory. A good choice of `batchSize`, therefore, depends on the size and sparseness of the VCF file and as well on the available memory. See Subsection 8.5 of the package vignette.
`noIndels` | if `TRUE` (default), only single nucleotide variants (SNVs) are considered and indels in the VCF file `Z` are skipped.
`onlyPass` | if `TRUE` (default), only variants are considered whose value in the `FILTER` column is “PASS”.
`na.limit` | all variants with a missing value ratio above this threshold in the VCF file `Z` are not considered.
`MAF.limit` | all variants with a minor allele frequency (MAF) above this threshold in the VCF file `Z` are not considered.
`na.action` | if “impute.major”, all missing values will be imputed by major alleles before association testing. If “omit”, all columns containing missing values in the VCF file `Z` are ignored.
`MAF.action` | if “invert”, all columns with an MAF exceeding 0.5 will be inverted in the sense that all minor alleles will be replaced by major alleles and vice versa. If “omit”, all variants in the VCF file with an MAF greater than 0.5 are ignored. If “ignore”, no action is taken and MAFs greater than 0.5 are kept as they are.
`sex` | if `NULL`, all samples are treated the same without any modifications; if `sex` is a factor with levels `F` (female) and `M` (male) that is as long as `length(model)`, this argument is interpreted as the sex of the samples. In this case, the genotypes corresponding to male samples are doubled before further processing. This is designed for mixed-sex analyses of the X chromosome outside of the pseudoautosomal regions.
`tmpdir` | if computations are parallelized over multiple client processes (see arguments `nnodes` and `cl`), the exchange of the null model object between the master process and the client processes is done via a temporary file. The `tmpdir` argument allows to specify into which directory the temporary file should be saved. On multi-core systems, the default should be sufficient. If the computations are distributed over a custom cluster, the `tmpdir` argument needs to be chosen such that all clients can access it via the same path.
`displayProgress` | if `TRUE` (default) and if `ranges` is a `GRangesList`, a progress message is printed upon completion of each list component (typically consisting of regions of one chromosome); this argument is ignored if `ranges` is not an object of class `GRangesList`.
`...` | all other parameters are passed on to the `assocTest` method with signature `TabixFile,NullModel`.

### Details

The `assocTest` method is the main function of the podkat package. For a given genotype and a null model, it performs the actual association test(s).

For null models of types “linear” and “logistic” (see `NullModel` and `nullModel`), a variance component score test is used (see Subsection 9.1 of the package vignette for details). The test relies on the choice of a particular kernel to measure the pairwise similarities of genotypes. The choice of the kernel can be made with the `kernel` argument (see `computeKernel` and Subsection 9.2 of the package vignette for more details). For null models of type “linear”, the test statistic follows a mixture of chi-squares distribution. For models of typ “logistic”, the test statistic approximately follows a mixture of chi-squares distribution. The computation of p-values for a given mixture of chi-squares can be done according to Davies (1980) (which is the default), according to Liu et al. (2009), or using a modified method similar to the one suggested by Liu et al. (2009) as implemented in the SKAT package, too. Which method is used can be controlled using the `method` argument. If method according to Davies (1980) fails, `assocTest` resorts to the method by Liu et al. (2009). See also Subsection 9.1 of the package vignette for more details.

For null models of type “logistic”, the `assocTest` method also offers the small sample correction suggested by Lee et al. (2012). Whether small sample correction is applied, is controlled by the `adj` argument. The additional adjustment of higher moments as suggested by Lee et al. (2012) is performed whenever resampled null model residuals are available in the null model `model` (slot `res.resampled.adj`, see `NullModel`). In this case, the `method` argument controls how the excess kurtosis of test statistics sampled from the null distribution are computed. The default setting “unbiased” computes unbiased estimates by using the exact expected value determined from the mixture components. The settings “population” and “sample” use almost unbiased and biased sample statistics, respectively. The choice “SKAT” uses the same method as implemented in the SKAT package. See Subsection 9.5 of the package vignette for more details.

If the null model is of type “bernoulli”, the test statistic follows a mixture of Bernoulli distributions. In this case, an exact p-value is determined that is computed as the probability to observe a test statistic for random Bernoulli-distributed traits (under the null hypothesis) that is at least as large as the observed test statistic. For reasons of computational complexity, this option is limited to sample numbers not larger than 100. See Subsection 9.1 of the package vignette for more details.

The podkat package offers multiple interfaces for association testing all of which require the second argument `model` to be a `NullModel` object. The simplest method is to call `assocTest` for an object of class `GenotypeMatrix` as first argument `Z`. If the `ranges` argument is not supplied, a single association test is performed using the entire genotype matrix contained in `Z` and an object of class `AssocTestResult` is returned. In this case, all variants need to reside on the same chromosome (compare with `computeKernel`). If the `ranges` argument is specified, each region in `ranges` is tested separately and the result is returned as an `AssocTestResultRanges` object.

As said, the simplest method is to store the entire genotype in a `GenotypeMatrix` object and to call `assocTest` as described above. This approach has the shortcoming that the entire genotype must be read (e.g. from a VCF file) and kept in memory as a whole. For large studies, in particular, whole-genome studies, this is not feasible. In order to be able to cope with large studies, the podkat package offers an interface that allows for reading from a VCF file piece by piece without the need to read and store the entire genotype at once. If `Z` is a `TabixFile` object or the name of a VCF file, `assocTest` reads from the file in batches of `batchSize` regions, performs the association tests for these regions, and returns the results as an `AssocTestResultRanges` object. This sequential batch processing can also be parallelized. The user can either set up a cluster him-/herself and pass the `SOCKcluster` object as `cl` argument. If the `cl` is `NULL`, users can leave the setup of the cluster to `assocTest`. In this case, the only thing necessary is to determine the number of R client processes by the `nnodes` argument. The variant with the VCF interface supports the same pre-processing and filter arguments as `readGenotypeMatrix` to control which variants are actually taken into account and how to handle variants with MAFs greater than 50%.

If the argument `Z` is a numeric matrix, `Z` is interpreted as a kernel matrix \bold{K}. Then a single association test is performed as described above and the result is returned as an `AssocTestResult` object. This allows the user to use a custom kernel not currently implemented in the podkat package. The `assocTest` function assumes that row and column objects in the kernel matrix are in the same order. It does not perform any check whether row and column names are the same or whether the kernel matrix is actually positive semi-definite. Users should be aware that running the function for invalid kernels matrices, i.e. for a matrix that is not positive semi-definite, produces meaningless results and may even lead to unexpected errors.

Finally, note that the samples in the null model `model` and in the genotype (`GenotypeMatrix` object or VCF file) need not be aligned to each other. If both the samples in `model` and in the genotype are named (i.e. row names are defined for `Z` if it is a `GenotypeMatrix` object; VCF files always contain sample names anyway), `assocTest` checks if all samples in `model` are present in the genotype. If so, it selects only those samples from the genotype that occur in the null model. If not, it quits with an error. If either the samples in the null model or the genotypes are not named, `assocTest` assumes that the samples are aligned to each other. This applies only if the number of samples in the null model and the number of genotypes are the same or if the number of genotypes equals the number of samples in the null model plus the number of samples that were omitted from the null model when it was trained (see `NullModel` and `nullModel`). Otherwise, the function quits with an error. An analogous procedure is applied if the kernel matrix interface is used (signature `matrix,NullModel`).

### Value

an object of class `AssocTestResult` or `AssocTestResultRanges` (see details above)

### Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

### References

http://www.bioinf.jku.at/software/podkat

Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., and Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82-93. DOI: 10.1016/j.ajhg.2011.05.029.

Lee, S., Emond, M. J., Bamshad, M. J., Barnes, K. C., Rieder, M. J., Nickerson, D. A., NHLBI Exome Sequencing Project - ESP Lung Project Team, Christiani, D. C., Wurfel, M. M., and Lin, X. (2012) Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am. J. Hum. Genet. 91, 224-237. DOI: 10.1016/j.ajhg.2012.06.007.

Davies, R. B. (1980) The distribution of a linear combination of chi^2 random variables. J. R. Stat. Soc. Ser. C-Appl. Stat. 29, 323-333.

Liu, H., Tang, Y., and Zhang, H. (2009) A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables. Comput. Stat. Data Anal. 53, 853-856.

### See Also

`AssocTestResult`, `AssocTestResultRanges`, `nullModel`, `NullModel`, `computeKernel`, `weightFuncs`, `readGenotypeMatrix`, `GenotypeMatrix`, `plot`, `qqplot`, `p.adjust`, `filterResult`

### Examples

    ## load genome description
    data(hgA)

    ## partition genome into overlapping windows
    windows <- partitionRegions(hgA)

    ## load genotype data from VCF file
    vcfFile <- system.file("examples/example1.vcf.gz", package="podkat")
    Z <- readGenotypeMatrix(vcfFile)

    ## read phenotype data from CSV file (continuous trait + covariates)
    phenoFile <- system.file("examples/example1lin.csv", package="podkat")
    pheno.c <- read.table(phenoFile, header=TRUE, sep=",")

    ## train null model with all covariates in data frame 'pheno'
    model.c <- nullModel(y ~ ., pheno.c)

    ## perform association test
    res <- assocTest(Z, model.c, windows)
    print(res)

    ## perform association test using the VCF interface
    res <- assocTest(vcfFile, model.c, windows, batchSize=100)
    print(res)

    ## create Manhattan plot of adjusted p-values
    plot(p.adjust(res), which="p.value.adj")

    ## read phenotype data from CSV file (binary trait + covariates)
    phenoFile <- system.file("examples/example1log.csv", package="podkat")
    pheno.b <-read.table(phenoFile, header=TRUE, sep=",")

    ## train null model with all covariates in data frame 'pheno'
    model.b <- nullModel(y ~ ., pheno.b)

    ## perform association test
    res <- assocTest(Z, model.b, windows)
    print(res)

    ## create Manhattan plot of adjusted p-values
    plot(p.adjust(res), which="p.value.adj")

[Package podkat version 1.18.0 Index]