snp.lhs.estimates {snpStats} | R Documentation

## Logistic regression 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 `"SnpMatrix"` as dependent variable, this function fits a logistic regression model. The Hardy-Weinberg assumption can be relaxed by use of a "robust" option.

### Usage

    snp.lhs.estimates(snp.data, base.formula, add.formula, subset, snp.subset,
                    data = sys.parent(), robust = FALSE, uncertain = FALSE,
                    control=glm.test.control())

### Arguments

`snp.data` | The SNP data, as an object of class `"SnpMatrix"` or `"XSnpMatrix"`
`base.formula` | A `formula` object describing a base model containing those terms which are to be fitted but for which parameter estimates are not required (the dependent variable is omitted from the model formula)
`add.formula` | A `formula` object describing the additional terms in the model for which parameter estimates are required (again, the dependent variable is 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`, Hardy-Weinberg equilibrium will is not assumed in calculating the variance-covariance matrix of parameter estimates
`uncertain` | If `TRUE`, uncertain genotypes are used and scored by their posterior expectations. Otherwise they are treated as missing. If set, this option forces `robust` variance estimates
`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`

### Details

The model fitted is the union of the `base.formula` and `add.formula` models, although parameter estimates (and their variance-covariance matrix) are only generated for the parameters of the latter. The "robust" option causes a Huber-White "sandwich" estimate of the variance-covariance matrix to be used in place of the usual inverse second derivative matrix of the log-likelihood (which assumes Hardy-Weinberg equilibrium). 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 `GlmEstimates`

### 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-covariance matrix of parameter estimates is calculated. No more than one `strata()` call may be used, and neither `strata(...)` or `cluster(...)` calls may appear in the `add.formula`.

If uncertain genotypes (e.g. as a result of imputation) are used, the interpretation of the regression coefficients is questionable.

A known bug is that the function fails when no `data` argument is supplied and the base model formula contains no variables (`~1`). A work-round is to create a data frame to hold the variables in the models and pass this as `data=`.

### Author(s)

David Clayton dc208@cam.ac.uk

### See Also

`GlmEstimates-class`, `snp.lhs.tests`

### Examples

    data(testdata)
    test1 <-
    snp.lhs.estimates(Autosomes[,1:10], ~cc, ~region, data=subject.data)
    test2 <-
    snp.lhs.estimates(Autosomes[,1:10], ~strata(region), ~cc,
       data=subject.data)
    test3 <-
    snp.lhs.estimates(Autosomes[,1:10], ~cc, ~region, data=subject.data, robust=TRUE)
    test4 <-
    snp.lhs.estimates(Autosomes[,1:10], ~strata(region), ~cc,
       data=subject.data, robust=TRUE)
    test5 <- snp.lhs.estimates(Autosomes[,1:10], ~region+sex, ~cc, data=subject.data, robust=TRUE)
    print(test1)
    print(test2)
    print(test3)
    print(test4)
    print(test5)

[Package snpStats version 1.36.0 Index]