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Network medicine-based epistasis detection in complex diseases: ready for quantum computing.
Hoffmann, Markus; Poschenrieder, Julian M; Incudini, Massimiliano; Baier, Sylvie; Fritz, Amelie; Maier, Andreas; Hartung, Michael; Hoffmann, Christian; Trummer, Nico; Adamowicz, Klaudia; Picciani, Mario; Scheibling, Evelyn; Harl, Maximilian V; Lesch, Ingmar; Frey, Hunor; Kayser, Simon; Wissenberg, Paul; Schwartz, Leon; Hafner, Leon; Acharya, Aakriti; Hackl, Lena; Grabert, Gordon; Lee, Sung-Gwon; Cho, Gyuhyeok; Cloward, Matthew E; Jankowski, Jakub; Lee, Hye Kyung; Tsoy, Olga; Wenke, Nina; Pedersen, Anders Gorm; Bønnelykke, Klaus; Mandarino, Antonio; Melograna, Federico; Schulz, Laura; Climente-González, Héctor; Wilhelm, Mathias; Iapichino, Luigi; Wienbrandt, Lars; Ellinghaus, David; Van Steen, Kristel; Grossi, Michele; Furth, Priscilla A; Hennighausen, Lothar; Di Pierro, Alessandra; Baumbach, Jan; Kacprowski, Tim; List, Markus; Blumenthal, David B.
Afiliación
  • Hoffmann M; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Poschenrieder JM; Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany.
  • Incudini M; National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA.
  • Baier S; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Fritz A; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Maier A; Dipartimento di Informatica, Universit'a di Verona, Strada le Grazie 15 - 34137 Verona, Italy.
  • Hartung M; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Hoffmann C; Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark.
  • Trummer N; Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
  • Adamowicz K; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Picciani M; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Scheibling E; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Harl MV; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Lesch I; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Frey H; Computational Mass Spectrometry, Technical University of Munich, Freising, Germany.
  • Kayser S; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Wissenberg P; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Schwartz L; Department of Health Sciences and Technology, Neuroscience Center Zürich (ZNZ), Swiss Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland.
  • Hafner L; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Acharya A; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Hackl L; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Grabert G; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Lee SG; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Cho G; Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
  • Cloward ME; Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany.
  • Jankowski J; Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany.
  • Lee HK; Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany.
  • Tsoy O; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Wenke N; Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics, Technische Universität Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany.
  • Pedersen AG; Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Rebenring 56, 38106 Braunschweig, Germany.
  • Bønnelykke K; National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA.
  • Mandarino A; School of Biological Sciences and Technology, Chonnam National University, Gwangju, Korea.
  • Melograna F; Department of Chemistry, Gwangju Institute of Science and Technology, Gwangju, Korea.
  • Schulz L; Department of Biology, Brigham Young University, Provo, UT, USA.
  • Climente-González H; National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA.
  • Wilhelm M; National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA.
  • Iapichino L; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Wienbrandt L; Institute for Computational Systems Biology, University of Hamburg, Germany.
  • Ellinghaus D; Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs. Lyngby, Denmark.
  • Van Steen K; Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark.
  • Grossi M; International Centre for Theory of Quantum Technologies, University of Gdansk, 80-309 Gdansk, Poland.
  • Furth PA; BIO3 - Systems Genetics; GIGA-R Medical Genomics, University of Liège, Liège, Belgium.
  • Hennighausen L; BIO3 - Systems Medicine; Department of Human Genetics, KU Leuven, Leuven, Belgium.
  • Di Pierro A; Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany.
  • Baumbach J; RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
  • Kacprowski T; Computational Mass Spectrometry, Technical University of Munich, Freising, Germany.
  • List M; Munich Data Science Institute (MDSI), Technical University of Munich, Garching, Germany.
  • Blumenthal DB; Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities (LRZ), Garching b. München, Germany.
Nucleic Acids Res ; 2024 Aug 23.
Article en En | MEDLINE | ID: mdl-39175109
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nucleic Acids Res Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Nucleic Acids Res Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido