ArticlePLoS genetics2020
Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.Frontiers in genetics · 2020Pooled it
- The gene expression landscape of disease genes.Genome biology · 2026Article
- Informed spatially aware patterns for multiplexed immunofluorescence data.Scientific reports · 2026Article
- Benchmarking methods integrating GWAS and single-cell transcriptomic data for mapping trait-cell type associations.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Fine-mapping causal tissues and genes at disease-associated loci.Nature genetics · 2025Article
- DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.Briefings in bioinformatics · 2024Article
- An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues.American journal of human genetics · 2024Article
- Fine-mapping causal tissues and genes at disease-associated loci.medRxiv : the preprint server for health sciences · 2024Article
- Conditional transcriptome-wide association study for fine-mapping candidate causal genes.Nature genetics · 2024Article
- Evaluating methods for integrating single-cell data and genetics to understand inflammatory disease complexity.Frontiers in immunology · 2024Article
- Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data.Cell genomics · 2023Article
- Modeling tissue co-regulation estimates tissue-specific contributions to disease.Nature genetics · 2023Article
- Joint gene network construction by single-cell RNA sequencing data.Biometrics · 2023Article
- How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis.Frontiers in psychiatry · 2023Article
- SpaceX: gene co-expression network estimation for spatial transcriptomics.Bioinformatics (Oxford, England) · 2022Article
- scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies.Genome biology · 2022Article
- Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling.iScience · 2022Article
- EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.PLoS genetics · 2022Article
- Functional annotation of breast cancer risk loci: current progress and future directions.British journal of cancer · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect measurements for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune 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.