Network medicine-based epistasis detection in complex diseases: ready for quantum computing
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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; Fitz, 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; 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.
medRxiv, 2023.Research output: Working paper › Preprint › Research
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TY - UNPB
T1 - Network medicine-based epistasis detection in complex diseases
T2 - ready for quantum computing
AU - Hoffmann, Markus
AU - Poschenrieder, Julian M
AU - Incudini, Massimiliano
AU - Baier, Sylvie
AU - Fitz, Amelie
AU - Maier, Andreas
AU - Hartung, Michael
AU - Hoffmann, Christian
AU - Trummer, Nico
AU - Adamowicz, Klaudia
AU - Picciani, Mario
AU - Scheibling, Evelyn
AU - Harl, Maximilian V
AU - Lesch, Ingmar
AU - Frey, Hunor
AU - Kayser, Simon
AU - Wissenberg, Paul
AU - Schwartz, Leon
AU - Hafner, Leon
AU - Acharya, Aakriti
AU - Hackl, Lena
AU - Grabert, Gordon
AU - Lee, Sung-Gwon
AU - Cho, Gyuhyeok
AU - Cloward, Matthew
AU - Jankowski, Jakub
AU - Lee, Hye Kyung
AU - Tsoy, Olga
AU - Wenke, Nina
AU - Pedersen, Anders Gorm
AU - Bønnelykke, Klaus
AU - Mandarino, Antonio
AU - Melograna, Federico
AU - Schulz, Laura
AU - Climente-González, Héctor
AU - Wilhelm, Mathias
AU - Iapichino, Luigi
AU - Wienbrandt, Lars
AU - Ellinghaus, David
AU - Van Steen, Kristel
AU - Grossi, Michele
AU - Furth, Priscilla A
AU - Hennighausen, Lothar
AU - Di Pierro, Alessandra
AU - Baumbach, Jan
AU - Kacprowski, Tim
AU - List, Markus
AU - Blumenthal, David B
PY - 2023
Y1 - 2023
N2 - 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 is the first application that 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.
AB - 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 is the first application that 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.
U2 - 10.1101/2023.11.07.23298205
DO - 10.1101/2023.11.07.23298205
M3 - Preprint
C2 - 38076997
BT - Network medicine-based epistasis detection in complex diseases
PB - medRxiv
ER -
ID: 397385131