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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detecting individual ancestry in the human genome
|
|---|---|
| Published in |
Investigative Genetics, May 2015
|
| DOI | 10.1186/s13323-015-0019-x |
| Pubmed ID | |
| Authors |
Andreas Wollstein, Oscar Lao |
| Abstract |
Detecting and quantifying the population substructure present in a sample of individuals are of main interest in the fields of genetic epidemiology, population genetics, and forensics among others. To date, several algorithms have been proposed for estimating the amount of genetic ancestry within an individual. In the present review, we introduce the most widely used methods in population genetics for detecting individual genetic ancestry. We further show, by means of simulations, the performance of popular algorithms for detecting individual ancestry in various controlled demographic scenarios. Finally, we provide some hints on how to interpret the results from these algorithms. |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | 27% |
| United States | 3 | 20% |
| Australia | 1 | 7% |
| Unknown | 7 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 53% |
| Scientists | 6 | 40% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | <1% |
| Sweden | 1 | <1% |
| United Kingdom | 1 | <1% |
| United States | 1 | <1% |
| Poland | 1 | <1% |
| Unknown | 98 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 28% |
| Researcher | 19 | 18% |
| Student > Master | 14 | 14% |
| Student > Bachelor | 12 | 12% |
| Professor > Associate Professor | 6 | 6% |
| Other | 14 | 14% |
| Unknown | 9 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 44 | 43% |
| Biochemistry, Genetics and Molecular Biology | 25 | 24% |
| Computer Science | 6 | 6% |
| Environmental Science | 3 | 3% |
| Medicine and Dentistry | 3 | 3% |
| Other | 11 | 11% |
| Unknown | 11 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 07 May 2015.
All research outputs
#4,107,843
of 24,862,067 outputs
Outputs from Investigative Genetics
#52
of 97 outputs
Outputs of similar age
#49,353
of 269,610 outputs
Outputs of similar age from Investigative Genetics
#2
of 3 outputs
Altmetric has tracked 24,862,067 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 97 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.3. This one is in the 46th percentile – i.e., 46% 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 269,610 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 81% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.