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Knowledge-based analysis of genetic associations of rheumatoid arthritis to inform studies searching for pleiotropic genes: a literature review and network analysis

Overview of attention for article published in Arthritis Research & Therapy, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

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5 tweeters
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

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11 Dimensions

Readers on

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45 Mendeley
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Title
Knowledge-based analysis of genetic associations of rheumatoid arthritis to inform studies searching for pleiotropic genes: a literature review and network analysis
Published in
Arthritis Research & Therapy, August 2015
DOI 10.1186/s13075-015-0715-1
Pubmed ID
Authors

Weiying Zheng, Shaoqi Rao

Abstract

Pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. Gene variants directly affect the normal processes of a series of physiological and biochemical reactions, and therefore cause a variety of diseases traits to be changed accordingly. Moreover, a shared genetic susceptibility mechanism may exist between different diseases. Therefore, shared genes, with pleiotropic effects, are important to understand the sharing pathogenesis and hence the mechanisms underlying comorbidity. In this study, we proposed combining genome-wide association studies (GWAS) and public knowledge databases to search for potential pleiotropic genes associated with rheumatoid arthritis (RA) and eight other related diseases. Here, a GWAS-based network analysis is used to recognize risk genes significantly associated with RA. These RA risk genes are re-extracted as potential pleiotropic genes if they have been proved to be susceptible genes for at least one of eight other diseases in the OMIM or PubMed databases. In total, we extracted 116 potential functional pleiotropic genes for RA and eight other diseases, including five hub pleiotropic genes, BTNL2, HLA-DRA, NOTCH4, TNXB, and C6orf10, where BTNL2, NOTCH4, and C6orf10 are novel pleiotropic genes identified by our analysis. This study demonstrates that pleiotropy is a common property of genes associated with disease traits. Our results ascertained the shared genetic risk profiles that predisposed individuals to RA and other diseases, which could have implications for identification of molecular targets for drug development, and classification of diseases.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 2%
Germany 1 2%
Unknown 43 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 18%
Student > Ph. D. Student 5 11%
Student > Bachelor 5 11%
Student > Doctoral Student 5 11%
Other 3 7%
Other 8 18%
Unknown 11 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 20%
Agricultural and Biological Sciences 8 18%
Medicine and Dentistry 6 13%
Nursing and Health Professions 4 9%
Computer Science 2 4%
Other 4 9%
Unknown 12 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 06 May 2019.
All research outputs
#3,598,620
of 15,060,518 outputs
Outputs from Arthritis Research & Therapy
#905
of 2,379 outputs
Outputs of similar age
#54,748
of 235,821 outputs
Outputs of similar age from Arthritis Research & Therapy
#1
of 1 outputs
Altmetric has tracked 15,060,518 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,379 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 235,821 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them