Bayesian test for co-localisation between pairs of genetic association studies using summary statistics
Claudia Giambartolomei (1), Damjan Vukcevic (2), Eric E. Schadt (3), Aroon D. Hingorani (1), Chris Wallace (4), Vincent Plagnol (1) ((1) University College London (UCL), London, UK, (2) Royal Children’s Hospital, Melbourne, Australia, (3) Mount Sinai School of Medicine, New York USA, (4) University of Cambridge, Cambridge, UK)
(Submitted on 17 May 2013)
Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. 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. A key feature of the method is the ability to derive the key output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at (this http URL). We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including > 100,000 individuals of European ancestry. Our co-localisation results are broadly consistent with the conclusion from the published meta-analysis. Combining all lipid biomarkers, our re-analysis supported 29 out of 38 reported co-localisation results with eQTLs. Two clearly discordant findings (IFT172, CPNE1), as well as multiple new co-localisation results, highlight the value of a formal systematic statistical test. Our findings provide information about the causal gene in associated intervals and have direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.