  Genetic association studies, in particular the genome-wide association study
design, have provided a wealth of novel insights into the aetiology of a wide
range of human diseases and traits. The next challenge consists of
understanding the molecular basis of these associations. The integration of
multiple association datasets, including gene expression datasets, can
contribute to this goal. We have developed a novel statistical methodology to
assess whether two association signals are consistent with a shared causal
variant. An application is the integration of disease scans with expression
quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be
integrated in this framework. We demonstrate the value of the approach by
reanalysing a gene expression dataset in 966 liver samples with a published
meta-analysis of lipid traits including >100, 000 individuals of European
ancestry. Combining all lipid biomarkers, our reanalysis supported 29 out of 38
reported colocalisation results with eQTLs and identified 14 new colocalisation
results, highlighting the value of a formal statistical test. In two cases of
reported eQTL-lipid pairs (IFT172, TBKBP1) for which our analysis suggests that
the eQTL pattern is not consistent with the lipid association, we identify
alternative colocalisation results with GCKR and KPNB1, indicating that these
genes are more likely to be causal in these genomic intervals. A key feature of
the method is the ability to derive the output statistics from single SNP
summary statistics, hence making it possible to perform systematic
meta-analysis type comparisons across multiple GWAS datasets
(http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about
candidate causal genes in associated intervals and has direct implications for
the understanding of complex diseases and the design of drugs to target disease
pathways.
