ArticleNucleic acids research2024
Network medicine-based epistasis detection in complex diseases: ready for quantum computing.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing.PLoS computational biology · 2026Article
- How Genomic and Structural Context Could Shape JAK-STAT Variant Pathogenicity.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2026Article
- Heritability and Transcriptional Impact of JAK3, STAT5A and STAT6 Variants in a Tyrolean Family.International journal of molecular sciences · 2026Article
- Turning heterogeneity of statistical epistasis networks to an advantage.Briefings in bioinformatics · 2026Article
- HapScoreDB: a database of protein language model functional scores for haplotype-resolved protein sequences.Nucleic acids research · 2026Article
- Impact of rare JAK/STAT germline mutations on vaccination-induced innate immune responses in a Tyrolian population.International journal of biological sciences · 2026Article
- Rare germline JAK/STAT variants in a Korean cohort amplify innate immune responses to vaccination.iScience · 2025Article
- Investigation of the transcriptional impact of rare germline JAK/STAT variants found in a Tyrolean alpine community.BMC genomics · 2025Article
- Can classical statistics and deep learning converge on explainable, causally driven target discovery?DNA research : an international journal for rapid publication of reports on genes and genomes · 2025Review
- Integrative Computational Analysis of Common EXO5 Haplotypes: Impact on Protein Dynamics, Genome Stability, and Cancer Progression.Journal of chemical information and modeling · 2025Article
- Data-driven projections of candidate enhancer-activating SNPs in immune regulation.BMC genomics · 2025Article
- Spotlight on amino acid changing mutations in the JAK-STAT pathway: from disease-specific mutation to general mutation databases.Scientific reports · 2025Article
- Considerations in the search for epistasis.Genome biology · 2024Review
- A primer for quantum computing and its applications to healthcare and biomedical research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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48 authors.
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.