SNPassoc (version 1.4-9)

permTest: Permutation test analysis

Description

This function extract the p values for permutation approach performed using scanWGassociation function

Usage

permTest(x, method="minimum", K)

Arguments

x

a required object of class 'WGassociation' with the attribute 'permTest'. See details

method

statistic used in the permutation test. The default is 'minimum' but 'rtp' (rank truncated product) is also available.

K

Value

• An object of class 'permTest'. 'print' returns a summary indicating the number of SNPs analyzed, the number of valid SNPs (those non-Monomorphic and that pass the calling rate), the p value after Bonferroni correction, and the p values based on permutation approach. One of them is based on considering the empirical percentil for the minimum p values, and the another one on assuming that the minimum p values follow a beta distribution. 'plot' produces a plot of the empirical distribution for the minimum p values (histogram) and the expected distribution assuming a beta distribution. The corrected p value is also showed in the plot. See examples for further illustration about all previous issues.

Details

This function extract the p values from an object of class 'WGassociation'. This object migth be obtained using the funcion called 'scanWGassociation' indicating the number of permutations in the argument 'nperm'.

References

Dudbridge F, Gusnanto A and Koeleman BPC. Detecting multiple associations in genome-wide studies. Human Genomics, 2006;2:310-317. Dudbridge F and Koeleman BPC. Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. Am J Hum Genet, 2004;75:424-435. JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

See Also

scanWGassociation

Examples

data(SNPs)
datSNP<-setupSNP(SNPs,6:40,sep="")
ans<-scanWGassociation(casco~1,data=datSNP,model="co",nperm=1000)

# pPerm<-permTest(ans)
# print(pPerm)
# plot(pPerm)