Evidence map›Paper›PMID 27287230›Full record

ArticleGenome medicine2016

Comprehensive promoter level expression quantitative trait loci analysis of the human frontal lobe.

Cornelis Blauwendraat, Margherita Francescatto, J Raphael Gibbs, Iris E Jansen, Javier Simón-Sánchez, Dena G Hernandez, Allissa A Dillman, Andrew B Singleton, Mark R Cookson, Patrizia Rizzu and 1 more

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 9% of its field
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

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.

  1. Pooled it
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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

11 authors at 5 institutions in 4 countries.

Cornelis BlauwendraatApplied Genomics for Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany. cornelis.blauwendraat@gmail.com.
Margherita FrancescattoGenome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
J Raphael GibbsLaboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA.
Iris E JansenGenome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
Javier Simón-SánchezGenome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
Dena G HernandezLaboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA.
Allissa A DillmanLaboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA.
Andrew B SingletonLaboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA.
Mark R CooksonLaboratory of Neurogenetics, National Institute on Aging (NIA), Bethesda, Maryland, USA.
Patrizia RizzuApplied Genomics for Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
Peter HeutinkGenome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
National Institute on Aging · USGerman Center for Neurodegenerative Diseases · DEAmsterdam UMC Location VUmc · NLHertie Institute for Clinical Brain Research · DEUniversity of Tübingen · DE

Funding

Characterization of Normal Genomic VariabilityZIAAG000932 · NIA · NATIONAL INSTITUTE ON AGING · PI SINGLETON, ANDREW · 2009 to 2024
$11.3M
Gene expression in the human brainZIAAG000947 · NIA · NATIONAL INSTITUTE ON AGING · PI COOKSON, MARK · 2009 to 2025
$7.2M
Genetic Analysis Of Non- Alzheimer's disease DementiasZIAAG000951 · NIA · NATIONAL INSTITUTE ON AGING · PI SINGLETON, ANDREW · 2009 to 2020
$5.6M
Characterization of Normal Genomic VariabilityZ01AG000932 · NIA · NATIONAL INSTITUTE ON AGING · PI SINGLETON, ANDREW · 2008 to 2008
$658k
EFFECT OF AGE AND GLYCEMIA ON TRANSCRIPTION FACTORSK01AG000947 · NIA · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI FUKAGAWA, NAOMI K · 2000 to 2004
$629k
REDUCING SERVICE USE IN OLDER PRIMARY CARE PATIENTSK23AG000932 · NIA · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI KLAPOW, JOSHUA C · 1999 to 2003
$493k
Intramural NIH HHS Z01 AG000932NIA NIH HHS K01 AG000947NIA NIH HHS K23 AG000932
6 · The paper itself

Abstract

backgroundExpression quantitative trait loci (eQTL) analysis is a powerful method to detect correlations between gene expression and genomic variants and is widely used to interpret the biological mechanism underlying identified genome wide association studies (GWAS) risk loci. Numerous eQTL studies have been performed on different cell types and tissues of which the majority has been based on microarray technology.

methodsWe present here an eQTL analysis based on cap analysis gene expression sequencing (CAGEseq) data created from human postmortem frontal lobe tissue combined with genotypes obtained through genotyping arrays, exome sequencing, and CAGEseq. Using CAGEseq as an expression profiling technique combined with these different genotyping techniques allows measurement of the molecular effect of variants on individual transcription start sites and increases the resolution of eQTL analysis by also including the non-annotated parts of the genome.

resultsWe identified 2410 eQTLs and show that non-coding transcripts are more likely to contain an eQTL than coding transcripts, in particular antisense transcripts. We provide evidence for how previously identified GWAS loci for schizophrenia (NRGN), Parkinson's disease, and Alzheimer's disease (PARK16 and MAPT loci) could increase the risk for disease at a molecular level. Furthermore, we demonstrate that CAGEseq improves eQTL analysis because variants obtained from CAGEseq are highly enriched for having a functional effect and thus are an efficient method towards the identification of causal variants.

conclusionOur data contain both coding and non-coding transcripts and has the added value that we have identified eQTLs for variants directly adjacent to TSS. Future eQTL studies would benefit from combining CAGEseq with RNA sequencing for a more complete interpretation of the transcriptome and increased understanding of eQTL signals.

Indexed as

Gene ExpressionQuantitative Trait LociAdolescentAdultAgedAged, 80 and overChildChild, PreschoolDatabases, GeneticFemaleFrontal LobeGenetic VariationGenotypeHigh-Throughput Nucleotide SequencingHumansMaleCap analysis gene expression sequencing (CAGEseq)Expression quantitative trait loci (eQTL)frontal lobe cortexNRGNPARK16

Identifiers

PMID27287230
PMCPMC4903003
OpenAlexW2409451325

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.