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Network reconstruction for trans acting genetic loci using multi-omics data and prior information

By Johann S Hawe, Ashis Saha, Melanie Waldenberger, Sonja Kunze, Simone Wahl, Martina Müller-Nurasyid, Holger Prokisch, Harald Grallert, Christian Herder, Annette Peters, Konstantin Strauch, Fabian J. Theis, Christian Gieger, John Chambers, Alexis Battle, Matthias Heinig

Posted 21 May 2020
bioRxiv DOI: 10.1101/2020.05.19.101592

Background: Molecular multi-omics data provide an in-depth view on biological systems, and their integration is crucial to gain insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). In these, regulatory network structures can leave specific footprints as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor, but use of biological prior information has been proposed to alleviate network inference. Previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. Results: We devised a strategy to integrate QTL with human population scale multi-omics data and comprehensively curated prior information from large-scale biological databases. State-of-the art network inference methods applied to these data and priors were then used, to recover the regulatory networks underlying trans-QTL hotspots. We benchmarked inference methods and showed, that Bayesian strategies using biologically-informed priors outperform methods without prior data in a simulation study and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generate novel functional hypotheses. Conclusion: We demonstrate, that existing biological knowledge can be leveraged for the integrative analysis of networks underlying trans associations to deduce novel hypotheses on cell regulatory mechanisms. ### Competing Interest Statement The authors have declared no competing interest.

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