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Genetic association studies, in particular the genome-wide association studydesign, have provided a wealth of novel insights into the aetiology of a widerange of human diseases and traits. The next challenge consists ofunderstanding the molecular basis of these associations. The integration ofmultiple association datasets, including gene expression datasets, cancontribute to this goal. We have developed a novel statistical methodology toassess whether two association signals are consistent with a shared causalvariant. An application is the integration of disease scans with expressionquantitative trait locus (eQTL) studies, but any pair of GWAS datasets can beintegrated in this framework. We demonstrate the value of the approach byreanalysing a gene expression dataset in 966 liver samples with a publishedmeta-analysis of lipid traits including >100, 000 individuals of Europeanancestry. Combining all lipid biomarkers, our reanalysis supported 29 out of 38reported colocalisation results with eQTLs and identified 14 new colocalisationresults, highlighting the value of a formal statistical test. In two cases ofreported eQTL-lipid pairs (IFT172, TBKBP1) for which our analysis suggests thatthe eQTL pattern is not consistent with the lipid association, we identifyalternative colocalisation results with GCKR and KPNB1, indicating that thesegenes are more likely to be causal in these genomic intervals. A key feature ofthe method is the ability to derive the output statistics from single SNPsummary statistics, hence making it possible to perform systematicmeta-analysis type comparisons across multiple GWAS datasets(http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information aboutcandidate causal genes in associated intervals and has direct implications forthe understanding of complex diseases and the design of drugs to target diseasepathways.

Bayesian Test for Colocalisation Between Pairs of Genetic Association Studies Using Summary Statistics