Evidence map›Paper›PMID 22139925›Full record

ArticleNucleic acids research2012

GWASdb: a database for human genetic variants identified by genome-wide association studies.

Mulin Jun Li, Panwen Wang, Xiaorong Liu, Ee Lyn Lim, Zhangyong Wang, Meredith Yeager, Maria P Wong, Pak Chung Sham, Stephen J Chanock, Junwen Wang

Abstract read
In one paragraph

Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.

0numbers the graph read from it
0cells of the map it votes in
129citing 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

129 citing papers in PubMed.

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  19. ALS is imprinted in the chromatin accessibility of blood cells.Cellular and molecular life sciences : CMLS · 2023
    Article
  20. Article

69 more citing papers are in PubMed but not listed here.

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

10 authors.

Mulin Jun LiDepartment of Biochemistry, The University of Hong Kong, Hong Kong SAR, China.
Panwen Wang
Xiaorong Liu
Ee Lyn Lim
Zhangyong Wang
Meredith Yeager
Maria P Wong
Pak Chung Sham
Stephen J Chanock
Junwen Wang

Funding

Intramural NIH HHS
6 · The paper itself

Abstract

Recent advances in genome-wide association studies (GWAS) have enabled us to identify thousands of genetic variants (GVs) that are associated with human diseases. As next-generation sequencing technologies become less expensive, more GVs will be discovered in the near future. Existing databases, such as NHGRI GWAS Catalog, collect GVs with only genome-wide level significance. However, many true disease susceptibility loci have relatively moderate P values and are not included in these databases. We have developed GWASdb that contains 20 times more data than the GWAS Catalog and includes less significant GVs (P < 1.0 × 10(-3)) manually curated from the literature. In addition, GWASdb provides comprehensive functional annotations for each GV, including genomic mapping information, regulatory effects (transcription factor binding sites, microRNA target sites and splicing sites), amino acid substitutions, evolution, gene expression and disease associations. Furthermore, GWASdb classifies these GVs according to diseases using Disease-Ontology Lite and Human Phenotype Ontology. It can conduct pathway enrichment and PPI network association analysis for these diseases. GWASdb provides an intuitive, multifunctional database for biologists and clinicians to explore GVs and their functional inferences. It is freely available at http://jjwanglab.org/gwasdb and will be updated frequently.

Indexed as

Databases, GeneticGenetic VariationGenome-Wide Association StudyChromosome MappingDiseaseHumansMolecular Sequence AnnotationUser-Computer Interface

Identifiers

PMID22139925
PMCPMC3245026

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Registered trials

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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.