Evidence map›Paper›PMID 25906321›Full record

ArticlePLoS genetics2015

Cross-population joint analysis of eQTLs: fine mapping and functional annotation.

Xiaoquan Wen, Francesca Luca, Roger Pique-Regi

Abstract read
In one paragraph

Article in PLoS genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

70 citing papers in PubMed.

  1. Article
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  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Improved multi-ancestry fine-mapping identifiesmedRxiv : the preprint server for health sciences · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Regulatory features aid interpretation of 3'UTR variants.American journal of human genetics · 2024
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
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  20. Article

10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xiaoquan WenDepartment of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Francesca LucaCenter for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA; Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA.
Roger Pique-RegiCenter for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA; Department of Clinical and Translational Sciences, Wayne State University, Detroit, MI, USA.

Funding

Functional Characterization of the Genetic and Environmental Determinants of Complex TraitsR01GM109215 · NIGMS · WAYNE STATE UNIVERSITY · PI Francesca Luca, Roger Pique-Regi · 2014 to 2026
$3.2M
Computational and statistical models for human geneticsR01HG007022 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2012 to 2016
$3.0M
Statistical analysis of gene expression quantitative trait loci (eQTL)R01MH101825 · NIMH · UNIVERSITY OF CHICAGO · PI STEPHENS, MATTHEW · 2013 to 2015
$1.1M
NHGRI NIH HHS HG007022NHGRI NIH HHS R01 HG007022NIGMS NIH HHS GM109215NIGMS NIH HHS R01 GM109215NIMH NIH HHS MH101825NIMH NIH HHS R01 MH101825
6 · The paper itself

Abstract

Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at molecular level. In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach effectively identifies multiple independent cis-eQTL signals that are consistent across populations, accounting for population heterogeneity in allele frequencies and linkage disequilibrium patterns. Furthermore, by integrating genomic annotations, our analysis framework enables high-resolution functional analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from five population groups. From this analysis, we concluded that i) jointly analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTL ii) many genes harbor multiple independent eQTLs in their cis regions iii) genetic variants that disrupt transcription factor binding are significantly enriched in eQTLs (p-value = 4.93 × 10(-22)).

Indexed as

Gene Expression RegulationGenetics, PopulationGenome-Wide Association StudyGene FrequencyGenetic Predisposition to DiseaseHumansLinkage DisequilibriumPhenotypePolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID25906321
PMCPMC4408026

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Registered trials

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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.