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

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 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.

SUBMITTER: Hoffmann M

PROVIDER: S-EPMC10705612 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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

Hoffmann Markus M Poschenrieder Julian M JM Incudini Massimiliano M Baier Sylvie S Fitz Amelie A Maier Andreas A Hartung Michael M Hoffmann Christian C Trummer Nico N Adamowicz Klaudia K Picciani Mario M Scheibling Evelyn E Harl Maximilian V MV Lesch Ingmar I Frey Hunor H Kayser Simon S Wissenberg Paul P Schwartz Leon L Hafner Leon L Acharya Aakriti A Hackl Lena L Grabert Gordon G Lee Sung-Gwon SG Cho Gyuhyeok G Cloward Matthew M Jankowski Jakub J Lee Hye Kyung HK Tsoy Olga O Wenke Nina N Pedersen Anders Gorm AG Bønnelykke Klaus K Mandarino Antonio A Melograna Federico F Schulz Laura L Climente-González Héctor H Wilhelm Mathias M Iapichino Luigi L Wienbrandt Lars L Ellinghaus David D Van Steen Kristel K Grossi Michele M Furth Priscilla A PA Hennighausen Lothar L Di Pierro Alessandra A Baumbach Jan J Kacprowski Tim T List Markus M Blumenthal David B DB

medRxiv : the preprint server for health sciences 20231109

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 netw ...[more]

PMID: 38076997

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Project description:Network medicine relies on different types of networks: from the molecular level of protein?protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein?protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs-including long non-coding RNAs (lncRNAs) -competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes-called switch genes-critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

Dataset Information

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

Publications

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

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