expect the null hypothesis of colocalisation has been rejected too readily, although this will affect loci with small and moderate effects to a greater degree than those with large effects. Thus, for our earlier analysis of colocalisation between T1D and monocyte gene expression signals [Wallace et al., 2012], the list of loci compatible with colocalisation are likely correct, but some loci were probably erroneously rejected, and re-analysis of these data will be required. 

In the case of network analysis, results can be difficult to reconcile with simple representations of the data. For example, integration of lung expression and asthma genetic association data led to the identification of GSDMA as the most likely causal gene for asthma in the 17q21 region [Hao et al., 2012], despite a graphical representation of the data showing that the SNPs most strongly associated with GSDMA expression were relatively weakly associated with asthma, and, vice versa , that the SNPs most strongly associated with asthma showed relatively weaker levels of association with GSDMA expression compared to the strongest signals. The asthma association on the 17q21 region was one of the first cases of explicitly using expression data to interpret disease association, with the association with asthma initially attributed to ORMDL3 based on expression data from EBV transformed cell lines [Moffatt et al., 2007] and subsequently to GSDMB from a reanalysis of the same data [Moffatt et al., 2010]. Candidate gene hypotheses have been constructed for all three genes. The lung expression data have greater potential for revealing the underlying gene, but, to hold confidence in results of analyses, particularly when the results contrast with simple visual inspection of the data, requires careful examination of the properties of the statistical method used. 

Given the tissue-specific nature of many eQTLs identified to date [Dimas et al., 2009; Fairfax et al., 2012], there is a need for more large, publicly available eQTL datasets in a variety of disease relevant tissues and purified cell subsets to support the interpretation of existing GWAS data. Although expression data is typically shared after the publication of an eQTL study, we note that the genetic data must also be made available to allow full integration of eQTL and disease signals at shared loci. The increasing abundance of substantial GWAS datasets and the increasing availability of large eQTL datasets [Fairfax et al., 2012; Fu et al., 2012; Hao et al., 2012], together with our reassurance that it is possible to conduct these tests whilst maintaining type 1 error rates and the availability of software in our R package will facilitate more widespread formal colocalisation testing. Integration of genetic association data has the potential to refine understanding of underlying genetic mechanisms and aid in the design of follow-up studies already 

