ArticleBMC bioinformatics2018
HT-eQTL: integrative expression quantitative trait loci analysis in a large number of human tissues.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A resource for integrated genomic analysis of the human liver.Scientific reports · 2022Pooled it
- A cross-tissue transcriptome-wide association study identifies novel susceptibility genes for atrial fibrillation.Journal of arrhythmia · 2025Article
- DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements.Computational and structural biotechnology journal · 2024Article
- DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.Genome medicine · 2023Article
- M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.PLoS genetics · 2021Article
- Stochastic imputation for integrated transcriptome association analysis of a longitudinally measured trait.Statistical methods in medical research · 2020Article
- Expression Quantitative Trait Loci Analysis in Multiple Tissues.Methods in molecular biology (Clifton, N.J.) · 2020Article
- A statistical framework for cross-tissue transcriptome-wide association analysis.Nature genetics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundExpression quantitative trait loci (eQTL) analysis identifies genetic markers associated with the expression of a gene. Most existing eQTL analyses and methods investigate association in a single, readily available tissue, such as blood. Joint analysis of eQTL in multiple tissues has the potential to improve, and expand the scope of, single-tissue analyses. Large-scale collaborative efforts such as the Genotype-Tissue Expression (GTEx) program are currently generating high quality data in a large number of tissues. However, computational constraints limit genome-wide multi-tissue eQTL analysis.
resultsWe develop an integrative method under a hierarchical Bayesian framework for eQTL analysis in a large number of tissues. The model fitting procedure is highly scalable, and the computing time is a polynomial function of the number of tissues. Multi-tissue eQTLs are identified through a local false discovery rate approach, which rigorously controls the false discovery rate. Using simulation and GTEx real data studies, we show that the proposed method has superior performance to existing methods in terms of computing time and the power of eQTL discovery.
conclusionsWe provide a scalable method for eQTL analysis in a large number of tissues. The method enables the identification of eQTL with different configurations and facilitates the characterization of tissue specificity.
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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.