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dc.contributor.authorHawe, Johann S.en_US
dc.contributor.authorSaha, Ashisen_US
dc.contributor.authorWaldenberger, Melanieen_US
dc.contributor.authorKunze, Sonjaen_US
dc.contributor.authorWahl, Simoneen_US
dc.contributor.authorMüller-Nurasyid, Martinaen_US
dc.contributor.authorProkisch, Holgeren_US
dc.contributor.authorGrallert, Haralden_US
dc.contributor.authorHerder, Christianen_US
dc.contributor.authorPeters, Annetteen_US
dc.contributor.authorStrauch, Konstantinen_US
dc.contributor.authorTheis, Fabian J.en_US
dc.contributor.authorGieger, Christianen_US
dc.contributor.authorChambers, John Campbellen_US
dc.contributor.authorBattle, Alexisen_US
dc.contributor.authorHeinig, Matthiasen_US
dc.identifier.citationHawe, 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).
dc.description.abstractBackground: 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.en_US
dc.description.sponsorshipMinistry of Health (MOH)en_US
dc.description.sponsorshipNational Medical Research Council (NMRC)en_US
dc.relation.ispartofGenome Medicineen_US
dc.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.en_US
dc.titleNetwork reconstruction for trans acting genetic loci using multi-omics data and prior informationen_US
dc.typeJournal Articleen
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en_US
dc.description.versionPublished versionen_US
dc.subject.keywordsData Integrationen_US
dc.subject.keywordsMachine Learningen_US
dc.description.acknowledgementOpen Access funding enabled and organized by Projekt DEAL. MH gratefully acknowledges funding by the Federal Ministry of Education and Research (BMBF, Germany) in the project eMed:confirm (01ZX1708G) and by the German Center of Cardiovascular Research (DZHK, BMBF grant number 81Z0600106). JC is supported by the Singapore Ministry of Health’s National Medical Research Council under its Singapore Translational Research Investigator (STaR) Award (NMRC/STaR/0028/2017). AB is supported by the NIH grant 1R01MH109905. The LOLIPOP study is supported by the National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the NIHR Official Development Assistance (ODA, award 16/136/68), the European Union FP7 (EpiMigrant, 279143), and H2020 programs (iHealth-T2D, 643774). The KORA study was initiated and financed by the Helmholtz Zentrum München-German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. The German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany), the Ministry of Culture and Science of the state North Rhine-Westphalia (Düsseldorf, Germany), and grants from the German Federal Ministry of Education and Research (Berlin, Germany) to the German Center for Diabetes Research e.V. (DZD).en_US
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