Functions to prepare principle component models for colocalisation testing
Prepares principal components of two datasets for colocalisation testing.
pcs.prepare(X1, X2, impute = TRUE)
X1 |
Either a SnpMatrix or numeric matrix of genetic data. Columns index SNPs, rows index samples. |
X2 |
as X1 |
impute |
if TRUE (default), impute missing genotypes |
If X1 and X2 are SnpMatrix objects, they are checked for
missing data, and any missing values imputed by repeated use of
impute.snps from the snpStats package.
Columns with common names are rbinded together and principal
components calculated using prcomp.
pcs.model can then be invoked to create glm objects.
a colocPCs object.
Chris Wallace
Wallace et al (2012). Statistical colocalisation of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum Mol Genet 21:2815-2824. http://europepmc.org/abstract/MED/22403184
Plagnol et al (2009). Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics 10:327-34. http://www.ncbi.nlm.nih.gov/pubmed/19039033
## simulate covariate matrix (X) and continuous response vector (Y) ## for two populations/triats Y1 and Y2 depend equally on f1 and f2 ## within each population, although their distributions differ between ## populations. They are compatible with a null hypothesis that they ## share a common causal variant, with the effect twice as strong for ## Y2 as Y1 set.seed(1) X1 <- matrix(rbinom(5000,1,0.4),ncol=10) Y1 <- rnorm(500,apply(X1[,1:2],1,sum),2) X2 <- matrix(rbinom(5000,1,0.6),ncol=10) Y2 <- rnorm(500,2*apply(X2[,1:2],1,sum),5) ## generate principal components object colnames(X1) <- colnames(X2) <- make.names(1:ncol(X1)) pcs <- pcs.prepare(X1,X2) ## generate glm objects m1 <- pcs.model(pcs, group=1, Y=Y1) m2 <- pcs.model(pcs, group=2, Y=Y2) ## test colocalisation using PCs coloc.test(m1,m2,plot.coeff=FALSE,bayes=FALSE)
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