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Pathway networks generated from human disease phenome

Overview of attention for article published in BMC Medical Genomics, September 2018
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3 tweeters

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Title
Pathway networks generated from human disease phenome
Published in
BMC Medical Genomics, September 2018
DOI 10.1186/s12920-018-0386-2
Pubmed ID
Authors

Ann G. Cirincione, Kaylyn L. Clark, Maricel G. Kann

Abstract

Understanding 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. We 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. By 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. This 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.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 2 22%
Researcher 2 22%
Student > Ph. D. Student 2 22%
Student > Master 1 11%
Student > Bachelor 1 11%
Other 1 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 67%
Computer Science 2 22%
Mathematics 1 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 September 2018.
All research outputs
#10,325,634
of 13,528,132 outputs
Outputs from BMC Medical Genomics
#493
of 687 outputs
Outputs of similar age
#184,370
of 264,645 outputs
Outputs of similar age from BMC Medical Genomics
#8
of 14 outputs
Altmetric has tracked 13,528,132 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 687 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.