Evidence map›Paper›PMID 29922679›Full record

ReviewFrontiers in cardiovascular medicine2018

Using Gene Expression to Annotate Cardiovascular GWAS Loci.

Matthias Heinig

Abstract readReview
In one paragraph

Review in Frontiers in cardiovascular medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Pulmonary circulation · 2023
    Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
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

1 author.

Matthias HeinigInstitute of Computational Biology, Helmholtz Zentrum München German Research Center for Environmental Health, Neuherberg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow up difficult. These non-protein-coding regions harbor regulatory sequences. Thus the study of genetic variants associated with transcription-so called expression quantitative trait loci-has emerged as a promising approach to identify regulatory sequence variants. The genes and pathways they control constitute candidate causal drivers at cardiovascular risk loci. This review provides an overview of the expression quantitative trait loci resources available for cardiovascular genetics research and the most commonly used approaches for candidate gene identification.

Indexed as

cardiovascular diseaseeQTLexpression quantitative trait locigenome wide association studyGWAS

Identifiers

PMID29922679
PMCPMC5996083

What OpenQuestion holds

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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.