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| Title |
Improving polygenic prediction in ancestrally diverse populations
|
|---|---|
| Published in |
Nature Genetics, May 2022
|
| DOI | 10.1038/s41588-022-01054-7 |
| Pubmed ID | |
| Authors |
Yunfeng Ruan, Yen-Feng Lin, Yen-Chen Anne Feng, Chia-Yen Chen, Max Lam, Zhenglin Guo, Lin He, Akira Sawa, Alicia R. Martin, Shengying Qin, Hailiang Huang, Tian Ge |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 20 | 16% |
| United Kingdom | 2 | 2% |
| Canada | 2 | 2% |
| South Africa | 1 | <1% |
| India | 1 | <1% |
| Azerbaijan | 1 | <1% |
| Pakistan | 1 | <1% |
| Taiwan | 1 | <1% |
| Bosnia and Herzegovina | 1 | <1% |
| Other | 8 | 6% |
| Unknown | 89 | 70% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 65 | 51% |
| Scientists | 49 | 39% |
| Practitioners (doctors, other healthcare professionals) | 10 | 8% |
| Science communicators (journalists, bloggers, editors) | 3 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 268 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 268 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 45 | 17% |
| Student > Ph. D. Student | 35 | 13% |
| Student > Master | 21 | 8% |
| Student > Doctoral Student | 13 | 5% |
| Student > Bachelor | 12 | 4% |
| Other | 42 | 16% |
| Unknown | 100 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 66 | 25% |
| Medicine and Dentistry | 22 | 8% |
| Agricultural and Biological Sciences | 20 | 7% |
| Neuroscience | 8 | 3% |
| Computer Science | 7 | 3% |
| Other | 31 | 12% |
| Unknown | 114 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 160. 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 01 May 2024.
All research outputs
#274,905
of 26,589,560 outputs
Outputs from Nature Genetics
#487
of 7,759 outputs
Outputs of similar age
#7,647
of 453,104 outputs
Outputs of similar age from Nature Genetics
#20
of 64 outputs
Altmetric has tracked 26,589,560 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,759 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.5. This one has done particularly well, scoring higher than 93% 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 453,104 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.