R Graphical Manual

Browse All

Last data update: 2014.03.03

R: 1-df and 2-df tests for genetic associations with SNPs

single.snp.tests

R Documentation

1-df and 2-df tests for genetic associations with 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).

Usage

single.snp.tests(phenotype, stratum, data = sys.parent(), snp.data, subset, snp.subset)

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
`subset`	A vector or expression describing the subset of subjects to be used in teh 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.

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.

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

A dataframe, with columns

`chi2.1df`	Cochran-Armitage type test for additive genetic component
`chi2.2df`	Chi-squared test for both additive and dominance components
`N`	The number of valid data points used

Note

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

Clayton (2008) Testing for association on the X chromosome Biostatistics (In press)

Examples

data(testdata)
results <- single.snp.tests(cc, stratum=region, data=subject.data,
   snp.data=Autosomes, snp.subset=1:10)
summary(results)
# QQ plot - see code{link{qq.chisq}}
qq.chisq(results$chi2.1df)
qq.chisq(results$chi2.2df)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(chopsticks)
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/chopsticks/single.snp.tests.Rd_%03d_medium.png", width=480, height=480)
> ### Name: single.snp.tests
> ### Title: 1-df and 2-df tests for genetic associations with SNPs
> ### Aliases: single.snp.tests
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(testdata)
> results <- single.snp.tests(cc, stratum=region, data=subject.data,
+    snp.data=Autosomes, snp.subset=1:10)
> summary(results)
    chi2.1df         chi2.2df          p.1df             p.2df       
 Min.   :0.0963   Min.   :0.8189   Min.   :0.08984   Min.   :0.1136  
 1st Qu.:0.7577   1st Qu.:1.0972   1st Qu.:0.23253   1st Qu.:0.2190  
 Median :1.0000   Median :1.5847   Median :0.31731   Median :0.4536  
 Mean   :1.1903   Mean   :2.1849   Mean   :0.33322   Mean   :0.4082  
 3rd Qu.:1.4253   3rd Qu.:3.2740   3rd Qu.:0.38406   3rd Qu.:0.5809  
 Max.   :2.8773   Max.   :4.3497   Max.   :0.75631   Max.   :0.6640  
 NA's   :1        NA's   :4        NA's   :1         NA's   :4       
       N        
 Min.   :378.0  
 1st Qu.:396.2  
 Median :398.5  
 Mean   :395.6  
 3rd Qu.:400.0  
 Max.   :400.0  
                
> # QQ plot - see code{qq.chisq}
> qq.chisq(results$chi2.1df)
       N  omitted   lambda 
9.000000 0.000000 4.867221 
> qq.chisq(results$chi2.2df)
      N omitted  lambda 
6.00000 0.00000 6.69583 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>