SNPMaP-package {SNPMaP} | R Documentation |
Pooling DNA on SNP microarrays is a cost-effective way to carry out genome-wide association studies for heritable disorders or traits. The SNPMaP package provides formal SNPMaP objects and methods in R as a base for these analyses using Affymetrix genotyping arrays.
Package: | SNPMaP |
Type: | Package |
Version: | 1.0.2 |
Date: | 2008-06-24 |
License: | GPL (>= 3) |
A SNPMaP study involves selecting individuals based on their disease status (case or control) or their
score on a quantitative trait (high or low extremes). DNA from these individuals is pooled in biological
replicate pools (20 pools of 50 different individuals each, for example). Each pool is then genotyped
according to the standard protocols on genotyping microarrays. The probe intensities from these arrays
can be extracted from the CEL files into SNPMaP
objects using the snpmap()
function and
Relative Allele Scores (RAS) can be calculated as estimates of allele frequency in each pool. By comparing
the allele frequencies in high and low or case and control pools, hundreds of thousands of SNPs across
the genome can be screened for association with a trait or disorder. The SNPMaP
package also
provides methods for visualising the data at each stage of the analysis. Using the lowMemory
option allows this to be done on a standard desktop computer (albeit slower than if all data is kept
in memory). This package is an evolution of the scripts referred to in Meaburn et al (2006) and is described
in Davis, Plomin and Schalkwyk (submitted for publication); please cite this paper if you find the package useful.
Additional supporting material is available at http://sgdp.iop.kcl.ac.uk/snpmap/.
Details of the arrays supported by the current version of SNPMaP
can be found in the SNPMaP.cdm
package.
Future releases of the package will build on the range of chips covered as well as introducing pre-packaged methods for carrying out the genome-wide screen for association.
Oliver SP Davis and Leo C Schalkwyk
Maintainer: Oliver SP Davis snpmap@iop.kcl.ac.uk
Davis, OSP, Plomin, R, and Schalkwyk, LC. (submitted for publication) The SNPMaP package for R:
A framework for genome-wide association using DNA pooling on microarrays.
Meaburn E, Butcher LM, Schalkwyk LC, and Plomin R. (2006) Genotyping pooled DNA using 100K SNP microarrays:
a step towards genomewide association scans. Nucleic Acids Research, 34(4):e28. http://dx.doi.org/10.1093/nar/gnj027
snpmap()
to set up a SNPMaP analysis.
SNPMaP-class
to represent a SNPMaP study.
SNPMaP.cdm-package
for the cdm
matrices that interpret the 'raw' format SNPMaP
objects.
affxparser-package
that reads the CEL files.
R.huge-package
that provides FileDoubleMatrices
for the lowMemory
option.
methods-package
for S4 formal classes.
## Not run: ## Getting started ## Creates the 'raw' SNPMaP object x on disk with mismatch probes included x<-snpmap(useMM=TRUE, RUN='cel2raw', lowMemory=TRUE) ## Print a summary of the SNPMaP object summary(x) ## Add a comment (prints in the summary) comment(x)<-'High and low extreme pools from January' ## View pseudo image to screen for artefacts image(x) ## Plot probe intensities plot(x, FUN=log) boxplot(x, FUN=log) ## tidy=TRUE removes the FileDoubleMatrix from the old x to keep the disk tidy x<-raw2ras(x, tidy=TRUE) ## Plot Relative Allele Scores plot(x) ## Default tidy=FALSE does not remove the original FileDoubleMatrix from disk ## Useful if you want to keep x (no side effects) y<-ras2rasS(x) ## View the first ten rows as.matrix(y[1:10,]) ## View a set of SNPs as.matrix(y[c("SNP_A-4192909", "SNP_A-4192918"),]) ## Transfer the SNPMaP object from disk to memory y<-disk2memory(y, tidy=TRUE) ## Run the analysis again from CEL files to RAS summaries without viewing intermediate stages ## This time in memory (may require a lot of RAM) z<-snpmap(useMM=TRUE, RUN='cel2rasS', lowMemory=FALSE) plot(z) ## Get the RAS summary scores as a standard matrix rasSummaries<-as.matrix(z) ## Read all the sets into a list allSets<-msnpmap(set=0) ## End(Not run)