is a web-application for performing, analysing and visualising genome-wide association studies, especially in model organisms. The web-application was originally developed at the Machine Learning and Computational Biology Research Group at the Max Planck Institute for Developmental Biology and Max Planck Institute for Intelligent Systems in Tübingen. In 2015 we moved to the ETH Zürich and continued the development of easyGWAS. In 2023 we moved easyGWAS to the Max Planck Institute for Biochemistry in Martinsried, Germany.
easyGWAS is designed to be a central resource for the genetics community that frees the user from the tedious tasks of implementing statistical software and managing data and computing resources.
It serves the community at large through easy data access, validation and reproduction of GWAS findings.
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Dominik is Professor at TUM Campus Straubing for Biotechnology and Sustainability. He studied bioinformatics at the Weihenstephan-Triesdorf University of Applied Sciences (2011), with stays abroad at the University of Cambridge, the European Bioinformatics Institute, UK and the University of New South Wales, Sydney, Australia. He received his PhD from the Max Planck Institute (MPI) for Developmental Biology, the MPI for Intelligent Systems, and the University of Tübingen (2015). After a postdoc at the D-BSSE of ETH Zurich (2016) and a short period in industry, he was appointed as professor of bioinformatics at TUM Campus Straubing and the Weihenstephan-Triesdorf Univerisity of Applied Sciences in 2018.
More details can be found here: https://bit.cs.tum.de.
Karsten Borgwardt is Director of the Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany since February 2023. Prior to that, we was a full professor at ETH Zurich from 2017-2023. His work won several awards, including the 1 million Euro Krupp Award for Young Professors in 2013 and a Starting Grant 2014 from the ERC-backup scheme of the Swiss National Science Foundation. Prof. Borgwardt has been leading large national and international research consortia, including the “Personalized Swiss Sepsis Study” (2018-2023) and the subsequent National Data Stream on infection-related outcomes in Swiss ICUs (2022-2023), and two Marie Curie Innovative Training Networks on Machine Learning in Medicine (2013-2016 and 2019-2022).
For his full CV and publication record, see his homepage at https://www.biochem.mpg.de/borgwardt