Evidence map›Paper›PMID 21398673›Full record

ArticleBioinformatics (Oxford, England)2011

PRIMe: a method for characterization and evaluation of pleiotropic regions from multiple genome-wide association studies.

Jie Huang, Andrew D Johnson, Christopher J O'Donnell

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 8 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

46 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Pleiotropic genes for metabolic syndrome and inflammation.Molecular genetics and metabolism · 2014
    Pooled it
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  5. Overlap between common genetic polymorphisms underpinning kidney traits and cardiovascular disease phenotypes: the CKDGen consortium.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2013
    Pooled it
  6. Pooled it
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  8. Abundant pleiotropy in human complex diseases and traits.American journal of human genetics · 2011
    Pooled it
  9. Review
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  13. Progress in the genetics of uveitis.Genes and immunity · 2022
    Review
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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

3 authors.

Jie HuangNational Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA.
Andrew D Johnson
Christopher J O'Donnell

Funding

Genomic Epidemiology of Cardiovascular DiseaseZIAHL006002 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI O'DONNELL, CHRISTOPHER · 2009 to 2015
$8.0M
Intramural NIH HHS
6 · The paper itself

Abstract

motivationThe concept of pleiotropy was proposed a century ago, though up to now there have been insufficient efforts to design robust statistics and software aimed at visualizing and evaluating pleiotropy at a regional level. The Pleiotropic Region Identification Method (PRIMe) was developed to evaluate potentially pleiotropic loci based upon data from multiple genome-wide association studies (GWAS).

methodsWe first provide a software tool to systematically identify and characterize genomic regions where low association P-values are observed with multiple traits. We use the term Pleiotropy Index to denote the number of traits with low association P-values at a particular genomic region. For GWAS assumed to be uncorrelated, we adopted the binomial distribution to approximate the statistical significance of the Pleiotropy Index. For GWAS conducted on traits with known correlation coefficients, simulations are performed to derive the statistical distribution of the Pleiotropy Index under the null hypothesis of no genotype-phenotype association. For six hematologic and three blood pressure traits where full GWAS results were available from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, we estimated the trait correlations and applied the simulation approach to examine genomic regions with statistical evidence of pleiotropy. We then applied the approximation approach to explore GWAS summarized in the National Human Genome Research Institute (NHGRI) GWAS Catalog.

resultsBy simulation, we identified pleiotropic regions including SH2B3 and BRAP (12q24.12) for hematologic and blood pressure traits. By approximation, we confirmed the genome-wide significant pleiotropy of these two regions based on the GWAS Catalog data, together with an exploration on other regions which highlights the FTO, GCKR and ABO regions. AVAILABILITY AND IMPLEMENTATION: The Perl and R scripts are available at http://www.framinghamheartstudy.org/research/gwas_pleiotropictool.html.

Indexed as

Genetic PleiotropyGenome-Wide Association StudySoftwareComputer SimulationGenome, HumanGenomicsHumansModels, StatisticalMultivariate AnalysisPhenotype

Identifiers

PMID21398673
PMCPMC3109517

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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.