ArticleNature communications2023
An analytical framework for decoding cell type-specific genetic variation of gene regulation.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Functional characterization of the GWAS lead SNP rs888663 and effects of GDF15 SNPs on GDF15 levels in gestational hypertension and preeclampsia.Molecular biology reports · 2026Article
- Global Evaluation of Congenital Heart Disease-Associated Non-Coding Variants.Research square · 2026Article
- Deep learning the dynamic regulatory sequence code of cardiac organoid differentiation.bioRxiv : the preprint server for biology · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Briefings in bioinformatics · 2024Review
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Mapping Cell Atlases at the Single-Cell Level.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- An analytical framework for decoding cell type-specific genetic variation of gene regulation.Nature communications · 2023Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A deeper understanding of genetic regulation and functional mechanisms underlying genetic associations with complex traits and diseases is impeded by cellular heterogeneity and linkage disequilibrium. To address these limits, we introduce Huatuo, a framework to decode genetic variation of gene regulation at cell type and single-nucleotide resolutions by integrating deep-learning-based variant predictions with population-based association analyses. We apply Huatuo to generate a comprehensive cell type-specific genetic variation landscape across human tissues and further evaluate their potential roles in complex diseases and traits. Finally, we show that Huatuo's inferences permit prioritizations of driver cell types associated with complex traits and diseases and allow for systematic insights into the mechanisms of phenotype-causal genetic variation.
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Registered trials
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