ArticleNature genetics2015
A gene-based association method for mapping traits using reference transcriptome data.
Article in Nature genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,233 papers, 8 of them syntheses that pooled it.
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.
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
1,233 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Multi-ancestry genome-wide association analyses of refractive error augment genetic discovery and polygenic prediction.Nature genetics · 2026Pooled it
- Genome and Transcriptome-Wide Analyses Identify Multiple Candidate Genes and a Significant Polygenic Contribution in Bicuspid Aortic Valve.Circulation · 2026Pooled it
- Global multi-ancestry genome-wide analyses identify genes and biological pathways associated with thyroid cancer and benign thyroid diseases.Nature genetics · 2026Pooled it
- Brain transcriptome-wide association studies in diverse ancestral populations reveal genes implicated in an anxiety-related phenotype.G3 (Bethesda, Md.) · 2026Pooled it
- Genomic and transcriptomic analyses of aortic stenosis enhance therapeutic target discovery and disease prediction.Nature genetics · 2026Pooled it
- Multi-ancestry meta-analysis of keloids uncovers novel susceptibility loci in diverse populations.Nature communications · 2025Pooled it
- A contextual genomic perspective on physical activity and its relationship to health, well being and illness.Nature genetics · 2025Pooled it
- Multiancestry Transcriptome-Wide Association Study Identifies Candidate Genes Associated with Hepatoblastoma.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Identification of hub genes involved in early-onset schizophrenia: from genetic susceptibility to predicted regulated gene expression.Molecular psychiatry · 2026Article
- High false sign rates in transcriptome-wide association studies.Nature genetics · 2026Article
- Co-regulation confounds direction of genetic effects in transcriptome-wide association studies.Nature genetics · 2026Article
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- GROMTools: scalable individual-level GReX imputation for mega-biobank-scale cohorts.Bioinformatics (Oxford, England) · 2026Article
- Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.European archives of psychiatry and clinical neuroscience · 2026Article
- Robust cis-by-trans epistasis in the human plasma proteome highlights an ABO-centered interaction network.American journal of human genetics · 2026Article
- Predicted brain-regional gene expression patterns in individuals living with Alzheimer's disease.Neurobiology of aging · 2026Article
- Genetically Predicted Blood DNA Methylation Reveals Putative Regulatory Signals Associated with ALS Risk.medRxiv : the preprint server for health sciences · 2026Article
1,173 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.
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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.