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Mendeley readers
Attention Score in Context
| Title |
FineMAV: prioritizing candidate genetic variants driving local adaptations in human populations
|
|---|---|
| Published in |
Genome Biology, January 2018
|
| DOI | 10.1186/s13059-017-1380-2 |
| Pubmed ID | |
| Authors |
Michał Szpak, Massimo Mezzavilla, Qasim Ayub, Yuan Chen, Yali Xue, Chris Tyler-Smith |
| Abstract |
We present a new method, Fine-Mapping of Adaptive Variation (FineMAV), which combines population differentiation, derived allele frequency, and molecular functionality to prioritize positively selected candidate variants for functional follow-up. We calibrate and test FineMAV using eight experimentally validated "gold standard" positively selected variants and simulations. FineMAV has good sensitivity and a low false discovery rate. Applying FineMAV to the 1000 Genomes Project Phase 3 SNP dataset, we report many novel selected variants, including ones in TGM3 and PRSS53 associated with hair phenotypes that we validate using available independent data. FineMAV is widely applicable to sequence data from both human and other species. |
X Demographics
The data shown below were collected from the profiles of 6 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 | 2 | 33% |
| Indonesia | 1 | 17% |
| France | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 50% |
| Members of the public | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 58 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 16% |
| Student > Ph. D. Student | 7 | 12% |
| Student > Bachelor | 7 | 12% |
| Student > Doctoral Student | 4 | 7% |
| Professor > Associate Professor | 4 | 7% |
| Other | 15 | 26% |
| Unknown | 12 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 29% |
| Biochemistry, Genetics and Molecular Biology | 16 | 28% |
| Medicine and Dentistry | 5 | 9% |
| Unspecified | 2 | 3% |
| Engineering | 2 | 3% |
| Other | 2 | 3% |
| Unknown | 14 | 24% |
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 25 July 2018.
All research outputs
#14,393,794
of 25,382,440 outputs
Outputs from Genome Biology
#3,819
of 4,468 outputs
Outputs of similar age
#218,724
of 451,258 outputs
Outputs of similar age from Genome Biology
#37
of 42 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 13th percentile – i.e., 13% 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 451,258 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 51% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.