single.snp.tests {snpMatrix} | R Documentation

## 1-df and 2-df tests for genetic associations with SNPs (or imputed SNPs)

### Description

This function carries out tests for association between phenotype and a series of single nucleotide polymorphisms (SNPs), within strata defined by a possibly confounding factor. SNPs are considered one at a time and both 1-df and 2-df tests are calculated. For a binary phenotype, the 1-df test is the Cochran-Armitage test (or, when stratified, the Mantel-extension test). The function will also calculate the same tests for SNPs imputed by regression analysis.

### Usage

    single.snp.tests(phenotype, stratum, data = sys.parent(), snp.data,
       rules=NULL, subset, snp.subset, score=FALSE)

### Arguments

`phenotype` | A vector containing the values of the phenotype
`stratum` | Optionally, a factor defining strata for the analysis
`data` | A dataframe containing the `phenotype` and `stratum` data. The row names of this are linked with the row names of the `snps` argument to establish correspondence of phenotype and genotype data. If this argument is not supplied, `phenotype` and `stratum` are evaluated in the calling environment and should be in the same order as rows of `snps`
`snp.data` | An object of class `"snp.matrix"` containing the SNP genotypes to be tested
`rules` | An object of class `"snp.reg.imputation"`. If supplied, the rules coded in this object are used, together with `snp.data`, to calculate tests for imputed SNPs
`subset` | A vector or expression describing the subset of subjects to be used in the analysis. This is evaluated in the same environment as the `phenotype` and `stratum` arguments
`snp.subset` | A vector describing the subset of SNPs to be considered. Default action is to test all SNPs in `snp.data` or, in imputation mode, as specified by `rules`
`score` | If `TRUE`, the output object will contain, for each SNP, the score vector and its variance-covariance matrix

### Details

Formally, the test statistics are score tests for generalized linear models with canonical link. That is, they are inner products between genotype indicators and the deviations of phenotypes from their stratum means. Variances (and covariances) are those of the permutation distribution obtained by randomly permuting phenotype within stratum.

When the function is used to calculate tests for imputed SNPs, the test is still a score test. The score statistics are calculated from the expected value, given observed SNPs, of the score statistic if the SNP to be tested were itself observed.

The `subset` argument can either be a logical vector of length equal to the length of the vector of phenotypes, an integer vector specifying positions in the `data` frame, or a character vector containing names of the selected rows in the `data` frame. Similarly, the `snp.subset` argument can be a logical, integer, or character vector.

### Value

An object of class `"snp.tests.single"`. If `score` is set to `TRUE`, the output object will be of the extended class `"snp.tests.single.score"` containing additional slots holding the score statistics and their variances (and covariances). This allows meta-analysis using the `pool` function.

### Note

The 1 df imputation tests are described by Chapman et al. (2008) and the 2 df imputation tests are a simple extension of these. The behaviour of this function for objects of class `X.snp.matrix` is as described by Clayton (2008). Males are treated as homozygous females and corrected variance estimates are used.

### Author(s)

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

### References

Chapman J.M., Cooper J.D., Todd J.A. and Clayton D.G. (2003) Human Heredity, 56:18-31.
Clayton (2008) Testing for association on the X chromosome Biostatistics, 9:593-600.)

### See Also

`snp.lhs.tests`, `snp.rhs.tests`, `impute.snps`, `snp.reg.imputation-class`, `pool`, `snp.tests.single-class`, `snp.tests.single.score-class`

### Examples

    data(testdata)
    results <- single.snp.tests(cc, stratum=region, data=subject.data,
       snp.data=Autosomes, snp.subset=1:10)
    summary(results)
    # QQ plot
    qq.chisq(chi.squared(results, 1), 1)
    qq.chisq(chi.squared(results, 2), 2)

[Package snpMatrix version 1.8.0 Index]