Network medicine-based epistasis detection in complex diseases: ready for quantum computing-Reference-Cited by-同舟云学术

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

Published:2024-08-23 Issue: Volume: Page:
ISSN:0305-1048
Container-title:Nucleic Acids Research
language:en
Short-container-title:

Author:

Hoffmann Markus¹²³^ORCID,Poschenrieder Julian M¹⁴^ORCID,Incudini Massimiliano⁵^ORCID,Baier Sylvie¹^ORCID,Fritz Amelie⁶⁷^ORCID,Maier Andreas⁴^ORCID,Hartung Michael⁴^ORCID,Hoffmann Christian⁴,Trummer Nico¹^ORCID,Adamowicz Klaudia⁴,Picciani Mario⁸^ORCID,Scheibling Evelyn¹,Harl Maximilian V¹⁹^ORCID,Lesch Ingmar¹,Frey Hunor¹,Kayser Simon¹,Wissenberg Paul¹,Schwartz Leon¹,Hafner Leon¹²^ORCID,Acharya Aakriti¹⁰¹¹,Hackl Lena⁴^ORCID,Grabert Gordon¹⁰¹¹^ORCID,Lee Sung-Gwon³¹²,Cho Gyuhyeok¹³,Cloward Matthew E¹⁴^ORCID,Jankowski Jakub³^ORCID,Lee Hye Kyung³^ORCID,Tsoy Olga⁴^ORCID,Wenke Nina⁴,Pedersen Anders Gorm⁶,Bønnelykke Klaus⁷,Mandarino Antonio¹⁵^ORCID,Melograna Federico¹⁶¹⁷,Schulz Laura¹⁸,Climente-González Héctor¹⁹^ORCID,Wilhelm Mathias⁸²⁰^ORCID,Iapichino Luigi¹⁸^ORCID,Wienbrandt Lars²¹^ORCID,Ellinghaus David²¹^ORCID,Van Steen Kristel¹⁶¹⁷,Grossi Michele²²^ORCID,Furth Priscilla A²³^ORCID,Hennighausen Lothar²³^ORCID,Di Pierro Alessandra⁵^ORCID,Baumbach Jan⁴²⁴^ORCID,Kacprowski Tim⁹¹⁰^ORCID,List Markus¹²⁵^ORCID,Blumenthal David B^ORCID

Affiliation:

1. Data Science in Systems Biology, School of Life Sciences, Technical University of Munich , Freising, Germany

2. Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich , D-85748 Garching, Germany

3. National Institute of Diabetes and Digestive and Kidney Diseases , Bethesda, MD 20892, USA

4. Institute for Computational Systems Biology, University of Hamburg , Germany

5. Dipartimento di Informatica, Universit‘a di Verona , Strada le Grazie 15 - 34137 Verona, Italy

6. Department of Health Technology, Section for Bioinformatics, Technical University of Denmark , DTU, 2800 Kgs. Lyngby, Denmark

7. Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen , Copenhagen, Denmark

8. Computational Mass Spectrometry, Technical University of Munich , Freising, Germany

9. Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich) , Zürich 8092, Switzerland

10. Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School , Rebenring 56, 38106 Braunschweig, Germany

11. Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig , Rebenring 56, 38106 Braunschweig, Germany

12. School of Biological Sciences and Technology, Chonnam National University , Gwangju, Korea

13. Department of Chemistry, Gwangju Institute of Science and Technology , Gwangju, Korea

14. Department of Biology, Brigham Young University , Provo, UT, USA

15. International Centre for Theory of Quantum Technologies, University of Gdańsk , 80-309 Gdańsk, Poland

16. BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège , Liège, Belgium

17. BIO3 - Systems Medicine; Department of Human Genetics , KU Leuven, Leuven, Belgium

18. Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ) , Garching b. München, Germany

19. RIKEN Center for Advanced Intelligence Project , Tokyo, Japan

20. Munich Data Science Institute (MDSI), Technical University of Munich , Garching, Germany

21. Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel , Kiel, Germany

22. European Organization for Nuclear Research (CERN ), Geneva1211, Switzerland

23. Departments of Oncology & Medicine, Georgetown University , Washington, DC, USA

24. Computational BioMedicine Lab, University of Southern Denmark , Denmark

25. Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg , Erlangen, Germany

Abstract

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.

Funder

German Excellence Initiative

National Institute of Diabetes and Digestive and Kidney Diseases

CERN Quantum Technology Initiative

Foundation for Polish Science

EU Smart Growth Operational Programme

Federal Ministry of Education and Research

Horizon 2020

Deutsche Forschungsgemeinschaft

Bavarian State Ministry of Science and the Arts

Munich Quantum Valley

European Union’s Horizon 2020