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Hawe, J. ; 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.* ; Battle, A.* ; Heinig, M.

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

Genome Med. 14:125 (2022)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Data Integration ; Machine Learning ; Multi-omics ; Network Inference ; Personalized Medicine ; Prior Information ; Simulation ; Systems Biology; KORA, Epigenetik, Expression, Genetik
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 1756-994X
e-ISSN 1756-994X
Zeitschrift Genome Medicine
Quellenangaben Band: 14, Heft: 1, Seiten: , Artikelnummer: 125 Supplement: ,
Verlag BioMed Central
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Epidemiology (EPI)
Institute of Genetic Epidemiology (IGE)
POF Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
PSP-Element(e) G-553500-001
G-504090-001
G-504091-001
G-504091-004
G-504100-001
G-504091-002
G-504000-010
Förderungen Singapore National Medical Research Council
DOI 10.1186/s13073-022-01124-9
WOS ID WOS:000879780800001
Scopus ID 85141522978
PubMed ID 36344995
Erfassungsdatum 2022-12-03
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