ArticleNature communications2022
Multi-context genetic modeling of transcriptional regulation resolves novel disease loci.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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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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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.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk.Genome medicine · 2024Pooled it
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Identifying condition-related cell-cell communication events using supervised tensor analysis.American journal of human genetics · 2026Article
- A Multi-Context Regulome-Wide Association Atlas for Genetic Studies of Aging Brain Disorders.medRxiv : the preprint server for health sciences · 2026Article
- FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data.Cell genomics · 2026Article
- Preimplantation genetic testing for polygenic diseases: A novel paradigm in embryo selection.Biochemistry and biophysics reports · 2026Review
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- Integrative Genomics Reveals Causal Pleiotropy and Therapeutic Opportunities at the Interface of Acute Pancreatitis and Infection.Journal of hepato-biliary-pancreatic sciences · 2026Article
- A Cross-Tissue Transcriptome Association Study Revealed Novel Susceptibility Genes for Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- Multi-ancestry genome-wide and transcriptome-wide association analyses identified new risk loci and genes for inflammatory bowel disease.medRxiv : the preprint server for health sciences · 2025Article
- scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.Research square · 2025Article
- Protocol to estimate the heritability of drug response with GxEMM and identify gene-drug interactions with TxEWAS.STAR protocols · 2025Article
- A Multi-omic study integrating plasma protein, multiple tissues, and single-cell identifies RNASET2 as a key gene for lung cancer.Discover oncology · 2025Article
- Leveraging pleiotropy for the improved treatment of psychiatric disorders.Molecular psychiatry · 2025Review
- scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.Nucleic acids research · 2025Article
- GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data.PLoS computational biology · 2025Article
- Characterizing the genetic architecture of drug response using gene-context interaction methods.Cell genomics · 2024Article
- Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study.Human genomics · 2024Article
- Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.Current protocols · 2024Article
- Single-cell genomics meets human genetics.Nature reviews. Genetics · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.
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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.