coloc (version 2.3-1)

coloc.test: Function to do colocalisation tests of two traits

Description

Performs the colocalisation tests described in Plagnol et al (2009) and Wallace et al (2012).

Usage

coloc.test(X, Y, vars.drop = NULL, ...)

Arguments

X

Either an lm or glm object for trait 1. The intersection of names(coefficients(X)) and names(coefficients(Y)) is used to identify SNPs in common which will be tested for colocalisation. Any Intercept term is dropped, but other covariates should have distinct names or be listed in vars.drop to avoid them being included in the colocalisation test.

Y

Either an lm or glm object for trait 2.

vars.drop

Character vector naming additional variables in either regression which are not SNPs and should not be used in the colocalisation test. They should appear in c(names(coefficients(X)),names(coefficients(Y)))

...

other arguments passed to coloc.test.summary().

Value

a numeric vector with 3 named elements:

eta.hat

The estimated slope.

chisquare

The chisquared test statistic

n

The number of snps used in the test. If eta were known, this would be the degrees of freedom of the test. Because eta has been replaced by its ML estimate, Plagnol et al suggest we expect the degrees of freedom to be n-1, but this requires the likelihood to be well behaved which is not always the case. We prefer to consider the posterior predictive p value.

ppp

The posterior predictive p value

Details

This is a test for proportionality of regression coefficients from two independent regressions. Analysis can either be based on a profile likelihood approach, where the proportionality coefficient, eta, is replaced by its maximum likelihood value, and inference is based on a chisquare test (p.value), or taking a hybrid-Bayesian approach and integrating the p value over the posterior distribution of eta, which gives a posterior predictive p value. The Bayesian approach can also be used to give a credible interval for eta. See the references below for further details.

References

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

Examples

## 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
set.seed(1)
  X1 <- matrix(rbinom(1000,1,0.4),ncol=2)
  Y1 <- rnorm(500,apply(X1,1,sum),2)
  X2 <- matrix(rbinom(1000,1,0.6),ncol=2)
  Y2 <- rnorm(500,2*apply(X2,1,sum),5)

  boxplot(list(Y1,Y2),names=c("Y1","Y2"))

  ## fit and store linear model objects
  colnames(X1) <- colnames(X2) <- c("f1","f2")
  summary(lm1 <- lm(Y1~f1+f2,data=as.data.frame(X1)))
  summary(lm2 <- lm(Y2~f1+f2,data=as.data.frame(X2)))

  ## test whether the traits are compatible with colocalisation
  ### ppp should be large (>0.05, for example), indicating that they are.
  par(mfrow=c(2,2))
  coloc.test(lm1,lm2,plot.coeff=TRUE,
             plots.extra=list(x=c("eta","theta"),
                              y=c("lhood","lhood")))