ArticleCell genomics2025
scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework.
Article in Cell genomics, 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.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Cross-trait mapping of shared susceptibility across inflammatory bowel disease and spondyloarthropathies.Journal of translational medicine · 2026Article
- Multi-Omics Integrative Analysis Identifies the NK Cell-STAT3 Axis as a Shared Immunogenetic Hub Underlying the Comorbidity of Primary Sclerosing Cholangitis and Ulcerative Colitis.Life (Basel, Switzerland) · 2026Article
- Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework.STAR protocols · 2026Article
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- Practical utility of sequence-to-omics models for improving the reproducibility of genetic fine-mapping.bioRxiv : the preprint server for biology · 2026Article
- Transcriptome-wide association studies at cell-state level using single-cell eQTL data.Cell genomics · 2026Article
- TWAS atlas 2.0: an updated data resource for transcriptome-wide association studies.Nucleic acids research · 2026Article
- Cell-type aware transcriptome-wide association study of mammographic density phenotypes.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
- Update of
Authors and funding
14 authors.
Funding
Abstract
Transcriptome-wide association studies (TWASs) help identify disease-causing genes but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here, we propose scPrediXcan, which integrates state-of-the-art deep learning approaches that predict epigenetic features from DNA sequences with the canonical TWAS framework. Our prediction approach, ctPred, predicts cell-type-specific expression with high accuracy and captures complex gene-regulatory grammar that linear models overlook. Applied to type 2 diabetes (T2D) and systemic lupus erythematosus (SLE), scPrediXcan outperformed the canonical TWAS framework by identifying more candidate causal genes, explaining more genome-wide association study (GWAS) loci and providing insights into the cellular specificity of TWAS hits. Overall, our results demonstrate that scPrediXcan represents a significant advance, promising to deepen our understanding of the cellular mechanisms underlying complex diseases.
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.