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

Hawe JS, Saha A, Waldenberger M, Kunze S, Wahl S, Mueller-Nurasyid M, Prokisch H, Grallert H, Herder C, Peters A, Strauch K, Theis FJ, Gieger C, Chambers J, Battle A, Heinig M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Genome Medicine BMC

Book Volume: 14

Article Number: 125

Journal Issue: 1

DOI: 10.1186/s13073-022-01124-9

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Johns Hopkins University (JHU) US United States (USA) (US) Technische Universität München (TUM) DE Germany (DE) Deutsches Zentrum für Diabetesforschung e.V. (DZD) DE Germany (DE) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Hawe, J.S., Saha, A., Waldenberger, M., Kunze, S., Wahl, S., Mueller-Nurasyid, M.,... Heinig, M. (2022). Network reconstruction for trans acting genetic loci using multi-omics data and prior information. Genome Medicine, 14(1). https://dx.doi.org/10.1186/s13073-022-01124-9

MLA:

Hawe, Johann S., et al. "Network reconstruction for trans acting genetic loci using multi-omics data and prior information." Genome Medicine 14.1 (2022).

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