snp.rhs.tests {snpStats} | R Documentation

## Score tests with SNP genotypes as independent variable

### Description

This function fits a generalized linear model with phenotype as dependent variable and, optionally, one or more potential confounders of a phenotype-genotype association as independent variable. A series of SNPs (or small groups of SNPs) are then tested for additional association with phenotype. In order to protect against misspecification of the variance function, "robust" tests may be selected.

### Usage

    snp.rhs.tests(formula, family = "binomial", link, weights, subset, data = parent.frame(),
       snp.data, rules=NULL, tests=NULL, robust = FALSE, uncertain=FALSE,
       control=glm.test.control(), allow.missing=0.01, score=FALSE)

### Arguments

`formula` | The base model formula, with phenotype as dependent variable
`family` | A string defining the generalized linear model family. This currently should (partially) match one of `"binomial"`, `"Poisson"`, `"Gaussian"` or `"gamma"` (case-insensitive)
`link` | A string defining the link function for the GLM. This currently should (partially) match one of `"logit"`, `"log"`, `"identity"` or `"inverse"`. The default action is to use the "canonical" link for the family selected
`data` | The dataframe in which the base model is to be fitted
`snp.data` | An object of class `"SnpMatrix"` or `"XSnpMatrix"` containing the SNP data
`rules` | An object of class `"ImputationRules"`. If supplied, the rules coded in this object are used, together with `snp.data`, to calculate tests for imputed SNPs
`tests` | Either a vector of SNP names (or numbers) for the SNPs to be tested, or a logical vector of length equal to the number of columns in `snp.data`, or a list of short numeric or character vectors defining groups of SNPs to be tested (see `Details`)
`weights` | "Prior" weights in the generalized linear model
`subset` | Array defining the subset of rows of `data` to use
`robust` | If `TRUE`, robust tests will be carried out
`uncertain` | If `TRUE`, uncertain genotypes are used and scored by their posterior expectations. Otherwise they are treated as missing
`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`
`allow.missing` | The maximum proportion of SNP genotype that can be missing before it becomes necessary to refit the base model
`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). The "base" model is first fitted, and a score test is performed for addition of one or more SNP genotypes to the model. Homozygous SNP genotypes are coded 0 or 2 and heterozygous genotypes are coded 1. For SNPs on the X chromosome, males are coded as homozygous females. For X SNPs, it will often be appropriate to include sex of subject in the base model (this is not done automatically).

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.

Usually SNPs to be used in tests will be referenced by name. However, they can also be referenced by number, a positive number indicating the appropriate column in the input `snp.data`, and a negative number indicating (minus) a position in the `rules` list. They can also be referenced by a logical selection vector of length equal to the number of columns in `snp.data`. Sets of tests involving more than one SNP are referenced by a list and can use a mixture of observed and imputed SNPs. If the `tests` argument is missing, single SNP tests are carried out; if a `rules` is given, all imputed SNP tests are calculated, otherwise all SNPs in the input `snp.data` matrix are tested. But note that, for single SNP tests, the function `single.snp.tests` will often achieve the same result much faster.

### Value

An object of class `GlmTests` or `GlmTestsScore` 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 `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 dc208@cam.ac.uk

### References

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

### See Also

`GlmTests-class`, `GlmTestsScore-class`, `single.snp.tests`, `snp.lhs.tests`, `impute.snps`, `ImputationRules-class`, `SnpMatrix-class`, `XSnpMatrix-class`

### Examples

    data(testdata)
    slt3 <- snp.rhs.tests(cc~strata(region), family="binomial",
       data=subject.data, snp.data= Autosomes, tests=1:10)
    print(slt3)

[Package snpStats version 1.30.0 Index]