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Title: Network reconstruction for trans acting genetic loci using multi-omics data and prior information
Authors: Hawe, Johann S.
Saha, Ashis
Waldenberger, Melanie
Kunze, Sonja
Wahl, Simone
Müller-Nurasyid, Martina
Prokisch, Holger
Grallert, Harald
Herder, Christian
Peters, Annette
Strauch, Konstantin
Theis, Fabian J.
Gieger, Christian
Chambers, John Campbell
Battle, Alexis
Heinig, Matthias
Keywords: Science::Medicine
Issue Date: 2022
Source: Hawe, J. S., Saha, A., Waldenberger, M., Kunze, S., Wahl, S., Müller-Nurasyid, M., Prokisch, H., Grallert, H., Herder, C., Peters, A., Strauch, K., Theis, F. J., Gieger, C., Chambers, J. C., Battle, A. & Heinig, M. (2022). Network reconstruction for trans acting genetic loci using multi-omics data and prior information. Genome Medicine, 14(1).
Project: NMRC/STaR/0028/2017 
Journal: Genome Medicine 
Abstract: Background: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining 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). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, 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. Methods: We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. Results: Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data 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 generated novel functional hypotheses. Conclusions: We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.
ISSN: 1756-994X
DOI: 10.1186/s13073-022-01124-9
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
Rights: © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver (http://creativeco applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles

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