Network medicine-based epistasis detection in complex diseases: Ready for quantum computing

Hoffmann M, Poschenrieder JM, Incudini M, Baier S, Fritz A, Maier A, Hartung M, Hoffmann C, Trummer N, Adamowicz K, Picciani M, Scheibling E, Harl MV, Lesch I, Frey H, Kayser S, Wissenberg P, Schwartz L, Hafner L, Acharya A, Hackl L, Grabert G, Lee SG, Cho G, Cloward ME, Jankowski J, Lee HK, Tsoy O, Wenke N, Pedersen AG, Bønnelykke K, Mandarino A, Melograna F, Schulz L, Climente-González H, Wilhelm M, Iapichino L, Wienbrandt L, Ellinghaus D, Van Steen K, Grossi M, Furth PA, Hennighausen L, Di Pierro A, Baumbach J, Kacprowski T, List M, Blumenthal DB (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 52

Pages Range: 10144-10160

Journal Issue: 17

DOI: 10.1093/nar/gkae697

Abstract

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Authors with CRIS profile

Involved external institutions

Technische Universität München (TUM) Germany (DE) University of Verona / Università degli Studi di Verona Italy (IT) Technical University of Denmark / Danmarks Tekniske Universitet (DTU) Denmark (DK) Universität Hamburg (UHH) Germany (DE) Peter L. Reichertz Institut für Medizinische Informatik (PLRI) Germany (DE) National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK / NIDDKD) United States (USA) (US) Gwangju Institute of Science and Technology (GIST) / 광주과학기술원 Korea, Republic of (KR) Brigham Young University United States (USA) (US) Gentofte Hospital Denmark (DK) University of Gdańsk / Uniwersytet Gdański Poland (PL) University of Liège (ULg) / Université de Liège Belgium (BE) Bayerische Akademie der Wissenschaften Germany (DE) RIKEN Center for Advanced Intelligence Project / 特定国立研究開発法人理化学研究所 革新知能統合研究センタ Japan (JP) Christian-Albrechts-Universität zu Kiel Germany (DE) Europäische Organisation für Kernforschung (CERN) Switzerland (CH) Georgetown University United States (USA) (US) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich Switzerland (CH)

How to cite

APA:

Hoffmann, M., Poschenrieder, J.M., Incudini, M., Baier, S., Fritz, A., Maier, A.,... Blumenthal, D.B. (2024). Network medicine-based epistasis detection in complex diseases: Ready for quantum computing. Nucleic Acids Research, 52(17), 10144-10160. https://doi.org/10.1093/nar/gkae697

MLA:

Hoffmann, Markus, et al. "Network medicine-based epistasis detection in complex diseases: Ready for quantum computing." Nucleic Acids Research 52.17 (2024): 10144-10160.

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