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On the identification of potential regulatory variants within genome wide association candidate SNP sets

Overview of attention for article published in BMC Medical Genomics, June 2014
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Title
On the identification of potential regulatory variants within genome wide association candidate SNP sets
Published in
BMC Medical Genomics, June 2014
DOI 10.1186/1755-8794-7-34
Pubmed ID
Authors

Chih-yu Chen, I-Shou Chang, Chao A Hsiung, Wyeth W Wasserman

Abstract

Genome wide association studies (GWAS) are a population-scale approach to the identification of segments of the genome in which genetic variations may contribute to disease risk. Current methods focus on the discovery of single nucleotide polymorphisms (SNPs) associated with disease traits. As there are many SNPs within identified risk loci, and the majority of these are situated within non-coding regions, a key challenge is to identify and prioritize variants affecting regulatory sequences that are likely to contribute to the phenotype assessed.

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Nigeria 1 <1%
Italy 1 <1%
Spain 1 <1%
Russia 1 <1%
Unknown 108 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 28%
Student > Ph. D. Student 19 17%
Student > Bachelor 14 12%
Professor > Associate Professor 13 11%
Student > Master 10 9%
Other 14 12%
Unknown 12 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 36%
Biochemistry, Genetics and Molecular Biology 33 29%
Medicine and Dentistry 11 10%
Computer Science 8 7%
Mathematics 2 2%
Other 6 5%
Unknown 13 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 September 2014.
All research outputs
#14,584,334
of 24,586,986 outputs
Outputs from BMC Medical Genomics
#529
of 1,332 outputs
Outputs of similar age
#115,569
of 233,567 outputs
Outputs of similar age from BMC Medical Genomics
#14
of 22 outputs
Altmetric has tracked 24,586,986 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,332 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 59% 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 233,567 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.