Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes.

Bioinformatics (Oxford, England) - 15 Jul 2017

Duong Dat, Gai Lisa, Snir Sagi, Kang Eun Yong, Han Buhm, Sul Jae Hoon, Eskin Eleazar

Abstract excerpt

MOTIVATION: There is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Computational Biology
Share to Gene Expression Profiling

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genetic Variation

Share to Genome-Wide Association Study

Share to Meta-Analysis as Topic

Share to Models, Genetic

Share to Quantitative Trait Loci

Share to Software

Source

Open in PubMed Open full text

Identifiers

PMID: 28881962
DOI: 10.1093/bioinformatics/btx227
PMCID: PMC5870567

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes.PMID 28881962Read ↗

Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretability.Semantic proximity Read ↗

HT-eQTL: integrative expression quantitative trait loci analysis in a large number of human tissues.Semantic proximity Read ↗

Partitioning and aggregating cross-tissue and tissue-specific genetic effects in identifying gene-trait associationsSemantic proximity Read ↗

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.Semantic proximity Read ↗

Multi-tissue polygenic models for transcriptome-wide association studiesSemantic proximity Read ↗

Redefining tissue specificity of genetic regulation of gene expression in the presence of allelic heterogeneity.Semantic proximity Read ↗

Subset-based method for cross-tissue transcriptome-wide association studies improves power and interpretabilitySemantic proximity Read ↗

Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLsSemantic proximity Read ↗

Tissue-Specific Enrichment Analysis (TSEA) to decode tissue specificitySemantic proximity Read ↗

Integrating predicted transcriptome from multiple tissues improves association detection.Semantic proximity Read ↗

Integrating Predicted Transcriptome From Multiple Tissues Improves Association DetectionSemantic proximity Read ↗

Expression Quantitative Trait Loci Analysis in Multiple Tissues.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.