Evidence map›Paper›PMID 20092628›Full record

ArticleBMC medical genomics2010

Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information.

Yonqing Zhang, Supriyo De, John R Garner, Kirstin Smith, S Alex Wang, Kevin G Becker

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
7.6field-weighted citation impact, top 2% 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

47 citing papers in PubMed, 147 citations in OpenAlex.

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  9. Induction of specific neuron types by overexpression of single transcription factors.In vitro cellular & developmental biology. Animal · 2016
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  13. ExAtlas: An interactive online tool for meta-analysis of gene expression data.Journal of bioinformatics and computational biology · 2015
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  14. Article
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  19. Article
  20. The integrin adhesome: from genes and proteins to human disease.Nature reviews. Molecular cell biology · 2014
    Review
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

6 authors at 3 institutions in 1 country.

Yonqing ZhangGene Expression and Genomics Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
Supriyo De
John R Garner
Kirstin Smith
S Alex Wang
Kevin G Becker
National Institutes of Health · USNational Institute on Aging · USInstitute on Aging · US

Funding

Bioinformatic Analysis of the Genetics of Common Complex DiseasesZICAG000613 · NIA · NATIONAL INSTITUTE ON AGING · PI DE, SUPRIYO · 2010 to 2025
$3.1M
Intramural NIH HHS
6 · The paper itself

Abstract

backgroundThe genetic contributions to human common disorders and mouse genetic models of disease are complex and often overlapping. In common human diseases, unlike classical Mendelian disorders, genetic factors generally have small effect sizes, are multifactorial, and are highly pleiotropic. Likewise, mouse genetic models of disease often have pleiotropic and overlapping phenotypes. Moreover, phenotypic descriptions in the literature in both human and mouse are often poorly characterized and difficult to compare directly.

methodsIn this report, human genetic association results from the literature are summarized with regard to replication, disease phenotype, and gene specific results; and organized in the context of a systematic disease ontology. Similarly summarized mouse genetic disease models are organized within the Mammalian Phenotype ontology. Human and mouse disease and phenotype based gene sets are identified. These disease gene sets are then compared individually and in large groups through dendrogram analysis and hierarchical clustering analysis.

resultsHuman disease and mouse phenotype gene sets are shown to group into disease and phenotypically relevant groups at both a coarse and fine level based on gene sharing.

conclusionThis analysis provides a systematic and global perspective on the genetics of common human disease as compared to itself and in the context of mouse genetic models of disease.

Indexed as

Genetic Association StudiesAnimalsCluster AnalysisDatabases, GeneticDiseaseDisease Models, AnimalHumansMicePhenotype

Identifiers

PMID20092628
PMCPMC2822734
OpenAlexW2007824099

What OpenQuestion holds

Textmetadata
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.