ArticleJournal of human genetics2008
Prediction of osteoporosis candidate genes by computational disease-gene identification strategy.
Article in Journal of human genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Analysis of susceptible genes and chromosome loci for lung cancers by automated gene prediction tools and genome scanning meta-analysis.International journal of clinical and experimental pathology · 2015Pooled it
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- Prioritization of retinal disease genes: an integrative approach.Human mutation · 2013Article
- Candidate gene identification for systemic lupus erythematosus using network centrality measures and gene ontology.PloS one · 2013Article
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- Identification of genes for bone mineral density variation by computational disease gene identification strategy.Journal of bone and mineral metabolism · 2011Article
- Bone mineral density is linked to 1p36 and 7p15-13 in a southern Chinese population.Journal of bone and mineral metabolism · 2011Article
- Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics.Human genetics · 2010Article
- Common variants in FLNB/CRTAP, not ARHGEF3 at 3p, are associated with osteoporosis in southern Chinese women.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2010Article
- Article
- Peroxisome proliferator-activated receptor gamma polymorphism is related to peak bone mass: the JPOS study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2010Article
- Linking genes to diseases: it's all in the data.Genome medicine · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis is a complex disease with a strong genetic component. To date, more than 20 genome-wide linkage scans across multiple populations have been launched to hunt for osteoporosis susceptibility genes. Some significant or suggestive chromosomal regions of linkage to bone mineral density have been identified and replicated in genome-wide linkage screens. However, identification of key candidate genes within these confirmed regions is challenging. We used five freely available bioinformatics tools (Prioritizer, GeneSeeker, PROSPECTR and SUSPECTS, Disease Gene Prediction, and Endeavor) to analyze the 13 well-replicated osteoporosis susceptibility loci: 1p36, 1q21-25, 2p22-24, 3p14-25, 4q25-34, 6p21, 7p14-21, 11q14-25, 12q23-24, 13q14-34, 20p12, 2q24-32, and 5q12-21. Pathways and regulatory network analyses were performed using the Ingenuity Pathways Analysis (IPA) software. We identified a subset of most likely candidate osteoporosis susceptibility genes that are largely involved in transforming growth factor (TGF)-beta signaling, granulocyte-macrophage colony-stimulating factor (GM-CSF) signaling, axonal guidance signaling, peroxisome proliferator-activated receptor (PPAR) signaling, and Wnt/beta-catenin signaling pathway. Six nonoverlapping networks were generated by IPA 5.0 from 88 out of the 91 candidate genes. The list of most likely candidate genes and the associated pathway identified will assist researchers in prioritizing candidate disease genes for further empirical analysis and understanding the pathogenesis of osteoporosis.
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Registered trials
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