ArticlePLoS genetics2013
A statistical framework for joint eQTL analysis in multiple tissues.
Article in PLoS genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 149 papers, 4 of them syntheses that pooled it.
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Who cites it
149 citing papers in PubMed, 4 syntheses or guidelines pooled it, 319 citations in OpenAlex.
- Trans-ancestry, Bayesian meta-analysis discovers 20 novel risk loci for inflammatory bowel disease in an African American, East Asian and European cohort.Human molecular genetics · 2023Pooled it
- Subset testing and analysis of multiple phenotypes.Genetic epidemiology · 2019Pooled it
- Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation.PloS one · 2015Pooled it
- Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS genetics · 2014Pooled it
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Dynamics of Gene and Allelic Expression During Modern Hybrid Maize Breeding.Plant biotechnology journal · 2026Article
- Diagnosing scientific replicability through probabilistic distinguishability.Bioinformatics (Oxford, England) · 2026Article
- Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping.Nature communications · 2026Article
- Towards improved fine-mapping of candidate causal variants.Nature reviews. Genetics · 2025Review
- A cross-tissue transcriptome-wide association study identifies novel susceptibility genes for atrial fibrillation.Journal of arrhythmia · 2025Article
- Beyond the Standard GWAS-A Guide for Plant Biologists.Plant & cell physiology · 2025Review
- Decoding eQTLs: unraveling their role in spondyloarthropathies pathogenesis and potential therapeutics.Frontiers in immunology · 2025Review
- Characterizing the genetic architecture of drug response using gene-context interaction methods.Cell genomics · 2024Article
- Distributed eQTL analysis with auxiliary information.Journal of statistical planning and inference · 2024Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- Estimating gene-level false discovery probability improves eQTL statistical fine-mapping precision.NAR genomics and bioinformatics · 2023Article
- Article
- Leveraging a surrogate outcome to improve inference on a partially missing target outcome.Biometrics · 2023Article
- The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle.Journal of animal science and biotechnology · 2023Article
- Pitfalls and opportunities for applying latent variables in single-cell eQTL analyses.Genome biology · 2023Article
89 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
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.
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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.