Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.

Data generated by genome-wide association studies (GWAS) are growing fast with the linkage of biobank samples to health records, and expanding capture of high-dimensional molecular phenotypes. However the utility of these efforts can only be fully realised if their complete results are collected from their heterogeneous sources and formats, harmonised and made programmatically accessible. Here we present the OpenGWAS database, an open source, open access, scalable and high-performance cloud-based data infrastructure that imports and publishes complete GWAS summary datasets and metadata for the scientific community. Our import pipeline harmonises these datasets against dbSNP and the human genome reference sequence, generates summary reports and standardises the format of results and metadata. Users can access the data via a website, an application programming interface, R and Python packages, and also as downloadable files that can be rapidly queried in high performance computing environments. OpenGWAS currently contains 126 billion genetic associations from 14,582 complete GWAS datasets representing a range of different human phenotypes and disease outcomes across different populations. We developed R and Python packages to serve as conduits between these GWAS data sources and a range of available analytical tools, enabling Mendelian randomization, genetic colocalisation analysis, fine mapping, genetic correlation and locus visualisation. OpenGWAS is freely accessible at https://gwas.mrcieu.ac.uk, and has been designed to facilitate integration with third party analytical tools.

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium (LD) is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.