The use of genetic data can be of great benefit in drug development. When analysed with appropriate statistical methods, such resources can be leveraged to identify potential drug targets and inform experimental trials [1]. It has been shown that drug development done with the backing of genetic data is more likely to be successful [2]. Increasingly, pharmacological studies are able to harness the results of genome-wide association studies (GWAS), which test for associations between a phenotype and genetic variation across the entire genome. Such studies are rapidly expanding in terms of both size of samples and range of phenotypes [3]. Although GWAS are able to identify many genetic variants that are associated with a phenotypic trait of interest, they are not able to provide, on their own, evidence as to which of these associations are causal, or by which mechanisms these associations come about. New statistical methodology is being developed which uses genetic data to help to answer these questions.