snp.lhs.tests {snpMatrix} | R Documentation

## Score tests with SNP genotypes as dependent variable

### Description

Under the assumption of Hardy-Weinberg equilibrium, a SNP genotype is a binomial variate with two trials for an autosomal SNP or with one or two trials (depending on sex) for a SNP on the X chromosome. With each SNP in an input `"snp.matrix"` as dependent variable, this function first fits a "base" logistic regression model and then carries out a score test for the addition of further term(s). The Hardy-Weinberg assumption can be relaxed by use of a "robust" option.

### Usage

    snp.lhs.tests(snp.data, base.formula, add.formula, subset, snp.subset,
                    data = sys.parent(), robust = FALSE,
                    control=glm.test.control(maxit=20, epsilon=1.e-4,R2Max=0.98),
                    score=FALSE)

### Arguments

`snp.data` | The SNP data, as an object of class `"snp.matrix"` or `"X.snp.matrix"`
`base.formula` | A `formula` object describing the base model, with dependent variable omitted
`add.formula` | A `formula` object describing the additional terms to be tested, also with dependent variable omitted
`subset` | An array describing the subset of observations to be considered
`snp.subset` | An array describing the subset of SNPs to be considered. Default action is to test all SNPs.
`data` | The data frame in which `base.formula`, `add.formula` and `subset` are to be evaluated
`robust` | If `TRUE`, a test which does not assume Hardy-Weinberg equilibrium will be used
`control` | An object giving parameters for the IRLS algorithm fitting of the base model and for the acceptable aliasing amongst new terms to be tested. See `glm.test.control`
`score` | Is extended score information to be returned?

### Details

The tests used are asymptotic chi-squared tests based on the vector of first and second derivatives of the log-likelihood with respect to the parameters of the additional model. The "robust" form is a generalized score test in the sense discussed by Boos(1992). If a `data` argument is supplied, the `snp.data` and `data` objects are aligned by rowname. Otherwise all variables in the model formulae are assumed to be stored in the same order as the columns of the `snp.data` object.

### Value

An object of class `snp.tests.glm` or `snp.tests.glm.score` depending on whether `score` is set to `FALSE` or `TRUE` in the call.

### Note

A factor (or several factors) may be included as arguments to the function `strata(...)` in the `base.formula`. This fits all interactions of the factors so included, but leads to faster computation than fitting these in the normal way. Additionally, a `cluster(...)` call may be included in the base model formula. This identifies clusters of potentially correlated observations (e.g. for members of the same family); in this case, an appropriate robust estimate of the variance of the score test is used.

### Author(s)

David Clayton david.clayton@cimr.cam.ac.uk

### References

Boos, Dennis D. (1992) On generalized score tests. The American Statistician, 46:327-333.

### See Also

`snp.tests.glm-class`, `snp.tests.glm.score-class`, `glm.test.control`,`snp.rhs.tests` `single.snp.tests`, `snp.matrix-class`, `X.snp.matrix-class`

### Examples

    data(testdata)
    snp.lhs.tests(Autosomes[,1:10], ~cc, ~region, data=subject.data)
    snp.lhs.tests(Autosomes[,1:10], ~strata(region), ~cc,
       data=subject.data)

[Package snpMatrix version 1.8.0 Index]