Dataset Information

Prioritization of disease microRNAs through a human phenome-microRNAome network.

ABSTRACT:

Background

The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.

Results

Herein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.

Conclusions

We presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.

SUBMITTER: Jiang Q

PROVIDER: S-EPMC2880408 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Publications

Prioritization of disease microRNAs through a human phenome-microRNAome network.

Jiang Qinghua Q Hao Yangyang Y Wang Guohua G Juan Liran L Zhang Tianjiao T Teng Mingxiang M Liu Yunlong Y Wang Yadong Y

BMC systems biology 20100528

<h4>Background</h4>The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.<h4>Results ...[more]

PMID: 20522252

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Project description:BACKGROUNDUnderstanding the effect of human genetic variations on disease can provide insight into phenotype-genotype relationships, and has great potential for improving the effectiveness of personalized medicine. While some genetic markers linked to disease susceptibility have been identified, a large number are still unknown. In this paper, we propose a pathway-based approach to extend disease-variant associations and find new molecular connections between genetic mutations and diseases.METHODSWe used a compilation of over 80,000 human genetic variants with known disease associations from databases including the Online Mendelian Inheritance in Man (OMIM), Clinical Variance database (ClinVar), Universal Protein Resource (UniProt), and Human Gene Mutation Database (HGMD). Furthermore, we used the Unified Medical Language System (UMLS) to normalize variant phenotype terminologies, mapping 87% of unique genetic variants to phenotypic disorder concepts. Lastly, variants were grouped by UMLS Medical Subject Heading (MeSH) identifiers to determine pathway enrichment in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.RESULTSBy linking KEGG pathways through underlying variant associations, we elucidated connections between the human genetic variant-based disease phenome and metabolic pathways, finding novel disease connections not otherwise detected through gene-level analysis. When looking at broader disease categories, our network analysis showed that large complex diseases, such as cancers, are highly linked by their common pathways. In addition, we found Cardiovascular Diseases and Skin and Connective Tissue Diseases to have the highest number of common pathways, among 35 significant main disease category (MeSH) pairings.CONCLUSIONSThis study constitutes an important contribution to extending disease-variant connections and new molecular links between diseases. Novel disease connections were made by disease-pathway associations not otherwise detected through single-gene analysis. For instance, we found that mutations in different genes associated to Noonan Syndrome and Essential Hypertension share a common pathway. This analysis also provides the foundation to build novel disease-drug networks through their underlying common metabolic pathways, thus enabling new diagnostic and therapeutic interventions.

Project description:BackgroundDisease complications, the onset of secondary phenotypes given a primary condition, can exacerbate the long-term severity of outcomes. However, the exact cause of many of these cross-phenotype associations is still unknown. One potential reason is shared genetic etiology-common genetic drivers may lead to the onset of multiple phenotypes. Disease-disease networks (DDNs), where nodes represent diseases and edges represent associations between diseases, can provide an intuitive way of understanding the relationships between phenotypes. Using summary statistics from a phenome-wide association study (PheWAS), we can generate a corresponding DDN where edges represent shared genetic variants between diseases. Such a network can help us analyze genetic associations across the diseasome, the landscape of all human diseases, and identify potential genetic influences for disease complications.ResultsTo improve the ease of network-based analysis of shared genetic components across phenotypes, we developed the humaN disEase phenoType MAp GEnerator (NETMAGE), a web-based tool that produces interactive DDN visualizations from PheWAS summary statistics. Users can search the map by various attributes and select nodes to view related phenotypes, associated variants, and various network statistics. As a test case, we used NETMAGE to construct a network from UK BioBank (UKBB) PheWAS summary statistic data. Our map correctly displayed previously identified disease comorbidities from the UKBB and identified concentrations of hub diseases in the endocrine/metabolic and circulatory disease categories. By examining the associations between phenotypes in our map, we can identify potential genetic explanations for the relationships between diseases and better understand the underlying architecture of the human diseasome. Our tool thus provides researchers with a means to identify prospective genetic targets for drug design, using network medicine to contribute to the exploration of personalized medicine.

Dataset Information

Prioritization of disease microRNAs through a human phenome-microRNAome network.

Background

Results

Conclusions

Publications

Prioritization of disease microRNAs through a human phenome-microRNAome network.

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