Introduction In recent years, genome-wide association studies (GWAS) have facilitated a dramatic increase in the number of genetic variants associated with human disease and other traits such as gene expression. Understanding the means by which these variants exert their effect will aid the design of the detailed functional followup studies already underway. Although the causal variants are not commonly known, multiple traits have been mapped to the same genetic loci, raising the possibility that the same variants affect multiple traits either directly or with one trait mediating the other. For example, genetic susceptibility to type 2 diabetes across 12 loci appears mediated by the genetic influence on body mass index [Li et al., 2011]. Within individual loci, researchers are examining the genetic association signals from pairs of traits in parallel, with similar results interpreted as evidence that the two traits may colocalise, or share a common causal variant. These traits may be eQTL signals across two or more tissues [Dimas et al., 2009; Fairfax et al., 2012], eQTL and disease signals [Nica et al., 2010; Wallace et al., 2012] or two or more diseases [Cotsapas et al., 2011]. Distinguishing cases where related diseases share a common causal variant versus those where neighbouring but distinct variants appear to underlie disease risk in a region will aid identification of cross-disease and disease-specific mechanisms. In addition, comparison of disease and eQTL data has the potential to reveal both the likely disease causal gene in regions where a number of candidate causal genes exist, and the relevant tissue type where tissue-specific eQTLs exist. However, dependence between genotypes at neighbouring SNPs, caused by LD, means that determination of colocalisation is not obvious, as there may exist distinct but neighbouring causal variants for each trait which are mutually associated. When these traits are measured in the same individuals, it is possible to use conditioning to determine whether one trait mediates the other [Li et al., 2011]. For example, if both body mass index (BMI) and type 2 diabetes (T2D) have been linked to a SNP, then when including BMI and the SNP as explanatory variables for T2D, BMI but not the SNP should show association if BMI is a mediator. However, when the traits are measured in distinct samples, or when two traits may share a common causal variant without one mediating the other, most researchers have approached the task of looking for colocalisation either by examining by eye the association signals across a set of common SNPs in the two data sets [Dubois et al., 2010] or by testing for evidence of residual 3