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Z2: Data management, synthesis, and integration

Prof. Dr. Zoran Nikoloski

Institute for Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam-Golm

Tel. +49-331-9776305, zoran.nikoloskiuni-potsdamde


Prof. Dr. Christoph Lippert

Chair for Digital Health-Machine Learning, Hasso-Plattner-Institute for Digital Engineering, Prof.-Dr.-Helmert-Straße 2-3, 14482 Potsdam

Tel. +49-331-5509-4850, Christoph.Lipperthpide


Open positions:

Position CRC1644/Z21– postdoc Lippert group

Position CRC1644/Z22– postdoc Nikoloski group



Z2 (Lippert, Nikoloski) will provide standardized approaches and pipelines to store, manage, analyse, and integrate the big data sets needed to study phenotypic plasticity as an evolving trait. To do so, the project will (i) generate a set of metadata templates compatible with the needs of the CRC and with NFDI DataPlant; (ii) create a set of customized scripts to facilitate reproducible analysis of reaction norms and their genetic basis; and (iii) develop cutting-edge computational approaches for data synthesis and use them to address overarching questions in plasticity research.


Project-related publications

Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011). FaST linear mixed models for genome-wide association studies. Nature Methods8(10), 833-835.

Cao, J., Schneeberger, K., Ossowski, S., Günther, T., Bender, S., Fitz, J., Koenig, D., Lanz, C., Stegle, O., Lippert, C., ... & Weigel, D. (2011). Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nature Genetics43(10), 956-963.

Listgarten, J., Lippert, C., Kadie, C. M., Davidson, R. I., Eskin, E., & Heckerman, D. (2012). Improved linear mixed models for genome-wide association studies. Nature Methods9(6), 525-526.

Zou, J., Lippert, C., Heckerman, D., Aryee, M., & Listgarten, J. (2014). Epigenome-wide association studies without the need for cell-type composition. Nature Methods11(3), 309-311.

Casale, F. P., Rakitsch, B., Lippert, C., & Stegle, O. (2015). Efficient set tests for the genetic analysis of correlated traits. Nature Methods12(8), 755-758.

Listgarten, J., Lippert, C., Kang, E. Y., Xiang, J., Kadie, C. M., & Heckerman, D. (2013). A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics29(12), 1526-1533.

Lippert, C., Xiang, J., Horta, D., Widmer, C., Kadie, C., Heckerman, D., & Listgarten, J. (2014). Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. Bioinformatics30(22), 3206-3214.

Listgarten, J., Lippert, C., & Heckerman, D. (2013). FaST-LMM-Select for addressing confounding from spatial structure and rare variants. Nature Genetics45(5), 470-471.

Grimm, D. G., Roqueiro, D., Salomé, P. A., Kleeberger, S., Greshake, B., Zhu, W., Liu, C., Lippert, C., ... & Borgwardt, K. M. (2017). easyGWAS: a cloud-based platform for comparing the results of genome-wide association studies. The Plant Cell29(1), 5-19.