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Softwares and methods for estimating genetic ancestry in human populations

Overview of attention for article published in Human Genomics, January 2013
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Mentioned by

twitter
2 tweeters
video
1 video uploader

Citations

dimensions_citation
62 Dimensions

Readers on

mendeley
258 Mendeley
citeulike
2 CiteULike
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Title
Softwares and methods for estimating genetic ancestry in human populations
Published in
Human Genomics, January 2013
DOI 10.1186/1479-7364-7-1
Pubmed ID
Authors

Yushi Liu, Toru Nyunoya, Shuguang Leng, Steven A Belinsky, Yohannes Tesfaigzi, Shannon Bruse

Abstract

The estimation of genetic ancestry in human populations has important applications in medical genetic studies. Genetic ancestry is used to control for population stratification in genetic association studies, and is used to understand the genetic basis for ethnic differences in disease susceptibility. In this review, we present an overview of genetic ancestry estimation in human disease studies, followed by a review of popular softwares and methods used for this estimation.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 258 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Brazil 3 1%
Portugal 1 <1%
France 1 <1%
Uruguay 1 <1%
Chile 1 <1%
South Africa 1 <1%
Canada 1 <1%
Argentina 1 <1%
Other 3 1%
Unknown 241 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 71 28%
Researcher 51 20%
Student > Master 32 12%
Student > Bachelor 28 11%
Student > Postgraduate 13 5%
Other 40 16%
Unknown 23 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 101 39%
Biochemistry, Genetics and Molecular Biology 52 20%
Medicine and Dentistry 17 7%
Computer Science 11 4%
Mathematics 9 3%
Other 27 10%
Unknown 41 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 December 2020.
All research outputs
#11,975,099
of 19,852,973 outputs
Outputs from Human Genomics
#234
of 400 outputs
Outputs of similar age
#89,087
of 165,787 outputs
Outputs of similar age from Human Genomics
#1
of 1 outputs
Altmetric has tracked 19,852,973 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 400 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 165,787 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
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