ArticleNature biotechnology2022
Variant to function mapping at single-cell resolution through network propagation.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed, 72 citations in OpenAlex.
- DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome.Journal of translational medicine · 2022Trial
- Article
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Genetic influences on haematopoiesis.Nature reviews. Genetics · 2026Review
- Reducing Unmet Needs in Hidradenitis Suppurativa by Including the Hair Follicle Among an Arsenal of Targets.Experimental dermatology · 2026Review
- Integrative analyses elucidate transcriptional regulatory functions of risk alleles for metabolic liver disease.Nature genetics · 2026Article
- RAG-mediated structural variation and its impact on relapse risk in acute lymphoblastic leukemia.medRxiv : the preprint server for health sciences · 2026Article
- Predicting enhancer-gene links from single-cell multi-omics data by integrating prior Hi-C information.Nucleic acids research · 2026Article
- Article
- Single-cell polygenic risk scores dissect cellular and molecular heterogeneity of complex human diseases.Nature biotechnology · 2026Article
- Connecting polygenic disease risk to cell states and regulatory programs through single-cell chromatin accessibility.bioRxiv : the preprint server for biology · 2026Article
- Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits.Nature communications · 2026Article
- Genetic Architecture of N-Terminal Pro-B-Type Natriuretic Peptide in a Multiancestry Study Population.Circulation. Genomic and precision medicine · 2026Article
- scVMAP: a comprehensive platform for integrating single-cell chromatin accessibility regions with causal variants.Nucleic acids research · 2026Article
- Genome-wide association study reveals genetic architecture and evolution of human retinal pigmentation.Science advances · 2026Article
- Linking single-cell multiomics with GWAS to reveal key regulators of disease risk.Nature aging · 2026Article
- Integrating polygenic signals and single-cell multiomics identifies cell-type-specific regulomes critical for immune- and aging-related diseases.Nature aging · 2026Article
- Decoding the germline genetic architecture of prostate cancer at a single cell resolution.PLoS genetics · 2025Article
- Article
- Integrative functional genomics reveals transcriptional regulatory function of risk alleles for metabolic liver disease.Research square · 2025Article
Corrections and comments
- Update of
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Genome-wide association studies in combination with single-cell genomic atlases can provide insights into the mechanisms of disease-causal genetic variation. However, identification of disease-relevant or trait-relevant cell types, states and trajectories is often hampered by sparsity and noise, particularly in the analysis of single-cell epigenomic data. To overcome these challenges, we present SCAVENGE, a computational algorithm that uses network propagation to map causal variants to their relevant cellular context at single-cell resolution. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation, applying the method to blood traits at distinct stages of human hematopoiesis, to monocyte subsets that increase the risk for severe Coronavirus Disease 2019 (COVID-19) and to intermediate lymphocyte developmental states that predispose to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.