Network medicine-based epistasis detection in complex diseases: ready for quantum computing.
Authors
Markus Hoffmann, Julian M Poschenrieder, Massimiliano Incudini, Sylvie Baier, Amelie Fritz, Andreas Maier, Michael Hartung, Christian Hoffmann, Nico Trummer, Klaudia Adamowicz, Mario Picciani, Evelyn Scheibling, Maximilian V Harl, Ingmar Lesch, Hunor Frey, Simon Kayser, Paul Wissenberg, Leon Schwartz, Leon Hafner, Aakriti Acharya, Lena Hackl, Gordon Grabert, Sung-Gwon Lee, Gyuhyeok Cho, Matthew E Cloward, Jakub Jankowski, Hye Kyung Lee, Olga Tsoy, Nina Wenke, Anders Gorm Pedersen, Klaus Bønnelykke, Antonio Mandarino, Federico Melograna, Laura Schulz, Héctor Climente-González, Mathias Wilhelm, Luigi Iapichino, Lars Wienbrandt, David Ellinghaus, Kristel Van Steen, Michele Grossi, Priscilla A Furth, Lothar Hennighausen, Alessandra Di Pierro, Jan Baumbach, Tim Kacprowski, Markus List, David B Blumenthal
Year of publication
2024Journal
NUCLEIC ACIDS RESVolume
52Issue
17Abstract
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.