pcs.model
Functions to prepare principle component models for colocalisation testing
pcs.model(object, group, Y, stratum = NULL, threshold = 0.8,
family = if (all(Y %in% c(0, 1))) { "binomial" } else {
"gaussian" })object |
A colocPCs object, result of |
group |
1 or 2, indicating which group of samples to extract from principal components matrix |
Y |
Numeric phenotype vector, length equal to the number of samples from the requested group |
stratum |
optional vector that gives stratum information |
threshold |
The minimum number of principal components which captures at
least threshold proportion of the variance will be selected. Simulations
suggest |
family |
Passed to |
Prepares models of response based on principal components of two datasets for colocalisation testing.
pcs.prepare returns a colocPCs object, pcs.model returns a glm 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) ## Alternatively, if one (or both) datasets have a known stratification, here simulated as S <- rbinom(500,1,0.5) ## specify this in pcs.model as m1 <- pcs.model(pcs, group=1, Y=Y1, stratum=S) ## test colocalisation using PCs coloc.test(m1,m2,plot.coeff=FALSE,bayes=FALSE)
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