ArticlePLoS computational biology2008
Prediction of human disease genes by human-mouse conserved coexpression analysis.
Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed, 139 citations in OpenAlex.
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- A Comparative Study of Gene Co-Expression Thresholding Algorithms.Journal of computational biology : a journal of computational molecular cell biology · 2024Article
- Systems genetics approaches for understanding complex traits with relevance for human disease.eLife · 2023Review
- Circular RNA- and microRNA-Mediated Post-Transcriptional Regulation of Preadipocyte Differentiation in Adipogenesis: From Expression Profiling to Signaling Pathway.International journal of molecular sciences · 2023Review
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- Approaches in Gene Coexpression Analysis in Eukaryotes.Biology · 2022Review
- Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning.European journal of human genetics : EJHG · 2021Article
- α/β-Hydrolase Domain (ABHD) Inhibitors as New Potential Therapeutic Options against Lipid-Related Diseases.Journal of medicinal chemistry · 2021Review
- A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data.BMC genomics · 2021Article
- Convergent and distributed effects of the 3q29 deletion on the human neural transcriptome.Translational psychiatry · 2021Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
- Interference with the Cannabinoid Receptor CB1R Results in Miswiring of GnRH3 and AgRP1 Axons in Zebrafish Embryos.International journal of molecular sciences · 2019Article
- A statistical normalization method and differential expression analysis for RNA-seq data between different species.BMC bioinformatics · 2019Article
- TarGo: network based target gene selection system for human disease related mouse models.Laboratory animal research · 2019Article
- Gene co-expression analysis for functional classification and gene-disease predictions.Briefings in bioinformatics · 2018Article
- Catalytic activities of mammalian epoxide hydrolases withJournal of lipid research · 2018Article
- Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network.BMC systems biology · 2017Article
- Computational prediction of human disease-related microRNAs by path-based random walk.Oncotarget · 2017Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEven in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. METHODOLOGY/PRINCIPAL
findingsWe show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.
conclusionOur results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.
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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.