Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244293v1?rss=1

Authors: Elsworth, B. L., Lyon, M. S., Alexander, T., Liu, Y., Matthews, P., Hallett, J., Bates, P., Palmer, T., Haberland, V., Davey Smith, G., Zheng, J., Haycock, P., Gaunt, T. R., Hemani, G.

Abstract:

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.

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