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

Autor: Hawe JS; Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.; German Heart Centre Munich, Department of Cardiology, Technical University Munich, Munich, Germany.; Department of Informatics, Technical University of Munich, Garching, Germany., Saha A; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA., Waldenberger M; Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany., Kunze S; Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany., Wahl S; Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany., Müller-Nurasyid M; Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.; IBE, Faculty of Medicine, LMU Munich, 81377, Munich, Germany.; Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.; Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany., Prokisch H; Institute of Human Genetics, School of Medicine, Technische Universität München, Munich, Germany., Grallert H; Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.; Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.; German Center for Diabetes Research (DZD), Neuherberg, Germany., Herder C; German Center for Diabetes Research (DZD), Neuherberg, Germany.; Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.; Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany., Peters A; Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany., Strauch K; Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.; Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.; Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany., Theis FJ; Department of Informatics, Technical University of Munich, Garching, Germany.; Department of Mathematics, Technical University of Munich, Garching, Germany., Gieger C; Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.; Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.; German Center for Diabetes Research (DZD), Neuherberg, Germany., Chambers J; Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.; Lee Kong Chian School of Medicine, Nanyang Technological University, 308232, Singapore, Singapore., Battle A; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA., Heinig M; Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany. matthias.heinig@helmholtz-muenchen.de.; Department of Informatics, Technical University of Munich, Garching, Germany. matthias.heinig@helmholtz-muenchen.de.; Munich Heart Association, Partner Site Munich, DZHK (German Centre for Cardiovascular Research), 10785, Berlin, Germany. matthias.heinig@helmholtz-muenchen.de.
Jazyk: angličtina
Zdroj: Genome medicine [Genome Med] 2022 Nov 07; Vol. 14 (1), pp. 125. Date of Electronic Publication: 2022 Nov 07.
DOI: 10.1186/s13073-022-01124-9
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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