Evidence map›Paper›PMID 25290266›Full record

SynthesisTranslational psychiatry2014

A meta-analysis of gene expression quantitative trait loci in brain.

Y Kim, K Xia, R Tao, P Giusti-Rodriguez, V Vladimirov, E van den Oord, P F Sullivan

Abstract readMeta-Analysis
In one paragraph

Synthesis in Translational psychiatry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 5 pooled it
–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

49 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  17. Genetic control of the human brain proteome.American journal of human genetics · 2021
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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

7 authors.

Y KimDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
K XiaDepartment of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
R TaoDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
P Giusti-RodriguezDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
V Vladimirov1] Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA [2] Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD, USA.
E van den OordCenter for Biomarker Research and Personalized Medicine; Virginia Commonwealth University, Richmond, VA, USA.
P F Sullivan1] Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA [2] Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA [3] Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Funding

1/2-Cis & Trans-Data Integration to Find Mechanisms Causing Psychiatric DisorderR01MH097283 · NIMH · VIRGINIA COMMONWEALTH UNIVERSITY · PI VAN DEN OORD, EDWIN · 2012 to 2014
$996k
NIMH NIH HHS R01 MH097283NIMH NIH HHS R01MH097283
6 · The paper itself

Abstract

Current catalogs of brain expression quantitative trait loci (eQTL) are incomplete and the findings do not replicate well across studies. All existing cortical eQTL studies are small and emphasize the need for a meta-analysis. We performed a meta-analysis of 424 brain samples across five studies to identify regulatory variants influencing gene expression in human cortex. We identified 3584 genes in autosomes and chromosome X with false discovery rate q<0.05 whose expression was significantly associated with DNA sequence variation. Consistent with previous eQTL studies, local regulatory variants tended to occur symmetrically around transcription start sites and the effect was more evident in studies with large sample sizes. In contrast to random SNPs, we observed that significant eQTLs were more likely to be near 5'-untranslated regions and intersect with regulatory features. Permutation-based enrichment analysis revealed that SNPs associated with schizophrenia and bipolar disorder were enriched among brain eQTLs. Genes with significant eQTL evidence were also strongly associated with diseases from OMIM (Online Mendelian Inheritance in Man) and the NHGRI (National Human Genome Research Institute) genome-wide association study catalog. Surprisingly, we found that a large proportion (28%) of ~1000 autosomal genes encoding proteins needed for mitochondrial structure or function were eQTLs (enrichment P-value=1.3 × 10(-9)), suggesting a potential role for common genetic variation influencing the robustness of energy supply in brain and a possible role in the etiology of some psychiatric disorders. These systematically generated eQTL information should be a valuable resource in determining the functional mechanisms of brain gene expression and the underlying biology of associations with psychiatric disorders.

Indexed as

BrainBipolar DisorderGene ExpressionGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPolymorphism, Single NucleotideQuantitative Trait LociSchizophrenia

Identifiers

PMID25290266
PMCPMC4350525

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.