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

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 mostl...

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Published inNucleic acids research Vol. 52; no. 17; pp. 10144 - 10160
Main Authors 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
Format Journal Article Web Resource
LanguageEnglish
Published England Oxford University Press 23.09.2024
Subjects
Computational Biology
Life sciences
Sciences du vivant
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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. Graphical Abstract Graphical Abstract
AbstractList 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. Graphical Abstract Graphical 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 ( ne twork-based e pistasis d etection via l ocal 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. Graphical 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.
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.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.
Author Adamowicz, Klaudia
Acharya, Aakriti
Wienbrandt, Lars
Hartung, Michael
Picciani, Mario
Melograna, Federico
List, Markus
Jankowski, Jakub
Bønnelykke, Klaus
Schulz, Laura
Hoffmann, Markus
Scheibling, Evelyn
Hackl, Lena
Grossi, Michele
Kacprowski, Tim
Wenke, Nina
Lesch, Ingmar
Hafner, Leon
Climente-González, Héctor
Tsoy, Olga
Furth, Priscilla A
Cho, Gyuhyeok
Poschenrieder, Julian M
Grabert, Gordon
Wissenberg, Paul
Lee, Sung-Gwon
Iapichino, Luigi
Hoffmann, Christian
Baier, Sylvie
Harl, Maximilian V
Hennighausen, Lothar
Ellinghaus, David
Pedersen, Anders Gorm
Baumbach, Jan
Mandarino, Antonio
Fritz, Amelie
Blumenthal, David B
Van Steen, Kristel
Wilhelm, Mathias
Lee, Hye Kyung
Frey, Hunor
Maier, Andreas
Incudini, Massimiliano
Schwartz, Leon
Kayser, Simon
Cloward, Matthew E
Di Pierro, Alessandra
Trummer, Nico
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Snippet Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic...
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SubjectTerms Computational Biology
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Sciences du vivant
Title Network medicine-based epistasis detection in complex diseases: ready for quantum computing
URI https://www.ncbi.nlm.nih.gov/pubmed/39175109
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http://orbi.ulg.ac.be/handle/2268/323609
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