ArticleNature genetics2025
Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance.
Article in Nature genetics, 2025. 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
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
9 citing papers in PubMed.
- A single-nuclei multiomics resource across four brain regions prioritises human neural cell types influencing brain-related traits.bioRxiv : the preprint server for biology · 2026Article
- Mapping enhancer-gene regulatory interactions from single-cell data.Nature genetics · 2026Article
- Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning.Cell genomics · 2026Article
- Distinguishing causal from tagging enhancers using single-cell multiome data.medRxiv : the preprint server for health sciences · 2026Article
- FAVOR 2.0: A reengineered functional annotation of variants online resource for interpreting genomic variation.Nucleic acids research · 2026Article
- Integrating polygenic signals and single-cell multiomics identifies cell-type-specific regulomes critical for immune- and aging-related diseases.Nature aging · 2026Article
- Integrative multi-omics dissection identifies ACO2, KLF5, and IMP4 as central regulators of the mitochondrial-immune axis in ulcerative colitis.Frontiers in immunology · 2026Article
- A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Variant-specific priors clarify colocalisation analysis.PLoS genetics · 2025Article
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
Methods that analyze single-cell paired RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) multiome data have shown promise in linking regulatory elements to genes. However, existing methods exhibit low concordance and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on expression quantitative trait locus (eQTL) data to assign a probabilistic score to each candidate single-nucleotide polymorphism-gene link. pgBoost attained higher enrichment than existing methods for evaluation sets derived from eQTL, activity-by-contact, CRISPR and genome-wide association study (GWAS) data. We further determined that restricting pgBoost to features from a focal cell type improved power to identify links relevant to that cell type. We highlight several examples in which pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies improves power to identify target genes underlying GWAS associations.
Indexed as
Identifiers
What Socratic 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 Socratic graph.