Evidence map›Paper›PMID 35192625›Full record

ArticlePloS one2022

Interpreting coronary artery disease GWAS results: A functional genomics approach assessing biological significance.

Katherine Hartmann, Michał Seweryn, Wolfgang Sadee

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.8field-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

11 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Single cell variant to enhancer to gene map for coronary artery disease.medRxiv : the preprint server for health sciences · 2024
    Article
  6. Article
  7. Bangladeshi Atherosclerosis Biobank and Hub: The BANGABANDHU Study.International journal of general medicine · 2024
    Article
  8. Article
  9. Review
  10. Review
  11. 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

3 authors at 2 institutions in 2 countries.

Katherine HartmannDepartment of Cancer Biology and Genetics, Center for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, United States of America.ORCID 0000-0002-1465-9653
Michał SewerynBiobank Lab, Department of Molecular Biophysics, University of Lodz, Lodz, Poland.
Wolfgang SadeeDepartment of Cancer Biology and Genetics, Center for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, United States of America.
The Ohio State University · USUniversity of Łódź · PL

Funding

Expression Genetics in Drug TherapyU01GM092655 · NIGMS · OHIO STATE UNIVERSITY · PI SADEE, WOLFGANG · 2010 to 2014
$8.6M
The Ohio State University Center for Clinical and Translational ScienceTL1TR001069 · NCATS · OHIO STATE UNIVERSITY · PI JACKSON, REBECCA D · 2013 to 2017
$763k
NCATS NIH HHS TL1 TR001069NIGMS NIH HHS U01 GM092655
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have implicated 58 loci in coronary artery disease (CAD). However, the biological basis for these associations, the relevant genes, and causative variants often remain uncertain. Since the vast majority of GWAS loci reside outside coding regions, most exert regulatory functions. Here we explore the complexity of each of these loci, using tissue specific RNA sequencing data from GTEx to identify genes that exhibit altered expression patterns in the context of GWAS-significant loci, expanding the list of candidate genes from the 75 currently annotated by GWAS to 245, with almost half of these transcripts being non-coding. Tissue specific allelic expression imbalance data, also from GTEx, allows us to uncover GWAS variants that mark functional variation in a locus, e.g., rs7528419 residing in the SORT1 locus, in liver specifically, and rs72689147 in the GUYC1A1 locus, across a variety of tissues. We consider the GWAS variant rs1412444 in the LIPA locus in more detail as an example, probing tissue and transcript specific effects of genetic variation in the region. By evaluating linkage disequilibrium (LD) between tissue specific eQTLs, we reveal evidence for multiple functional variants within loci. We identify 3 variants (rs1412444, rs1051338, rs2250781) that when considered together, each improve the ability to account for LIPA gene expression, suggesting multiple interacting factors. These results refine the assignment of 58 GWAS loci to likely causative variants in a handful of cases and for the remainder help to re-prioritize associated genes and RNA isoforms, suggesting that ncRNAs maybe a relevant transcript in almost half of CAD GWAS results. Our findings support a multi-factorial system where a single variant can influence multiple genes and each genes is regulated by multiple variants.

Indexed as

Genetic Predisposition to DiseaseQuantitative Trait LociAdaptor Proteins, Vesicular TransportAllelesAllelic ImbalanceCoronary Artery DiseaseGene ExpressionGenome-Wide Association StudyGenomicsHumansLinkage DisequilibriumPolymorphism, Single NucleotideRisk FactorsRNA, MessengerSortilinSterol EsteraseAdaptor Proteins, Vesicular TransportLIPA protein, humanRNA, MessengerSortilinSterol Esterase

Identifiers

PMID35192625
PMCPMC8863290
OpenAlexW4226054252

What OpenQuestion holds

Textmetadata
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.