SynthesisPLoS genetics2013
Effectively identifying eQTLs from multiple tissues by combining mixed model and meta-analytic approaches.
Synthesis in PLoS genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 5 of them syntheses 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
73 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- 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
- Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits.Nature genetics · 2022Pooled it
- Meta-analysis of SNP-environment interaction with heterogeneity for overlapping data.Scientific reports · 2021Pooled it
- Japanese GWAS identifies variants for bust-size, dysmenorrhea, and menstrual fever that are eQTLs for relevant protein-coding or long non-coding RNAs.Scientific reports · 2018Pooled it
- Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation.PloS one · 2015Pooled it
- Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Trial
- Tissue-specific expression and regulation of congenital disorders of glycosylation genes: A GTEx-based in silico study.Molecular genetics and metabolism reports · 2026Article
- FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data.Cell genomics · 2026Article
- Orchestrating multi-state QTL analysis with Bioconductor.BMC bioinformatics · 2026Article
- A novel machine learning-based algorithm for eQTL identification reveals complex pleiotropic effects in the MHC region.Briefings in bioinformatics · 2026Article
- A cross-tissue transcriptome-wide association study identifies novel susceptibility genes for atrial fibrillation.Journal of arrhythmia · 2025Article
- GLP1R Gene Expression and Kidney Disease Progression.JAMA network open · 2024Article
- Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes.Nature communications · 2024Article
- Distributed eQTL analysis with auxiliary information.Journal of statistical planning and inference · 2024Article
- Gene expression and RNA splicing explain large proportions of the heritability for complex traits in cattle.Cell genomics · 2023Article
- Alterations in Growth Habit to Channel End-of-Season Perennial Reserves towards Increased Yield and Reduced Regrowth after Defoliation in Upland Cotton (International journal of molecular sciences · 2023Article
- Hypergraph factorization for multi-tissue gene expression imputation.Nature machine intelligence · 2023Article
- Leveraging a surrogate outcome to improve inference on a partially missing target outcome.Biometrics · 2023Article
- Germline Cancer Gene Expression Quantitative Trait Loci Are Associated with Local and Global Tumor Mutations.Cancer research · 2023Article
- Leveraging drug perturbation to reveal genetic regulators of hepatic gene expression in African Americans.American journal of human genetics · 2023Article
13 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
5 authors.
Funding
Abstract
Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue.
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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.