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Mendeley readers
Attention Score in Context
| Title |
Weighted selective collapsing strategy for detecting rare and common variants in genetic association study
|
|---|---|
| Published in |
BMC Genomic Data, February 2012
|
| DOI | 10.1186/1471-2156-13-7 |
| Pubmed ID | |
| Authors |
Yilin Dai, Renfang Jiang, Jianping Dong |
| Abstract |
Genome-wide association studies (GWAS) have been used successfully in detecting associations between common genetic variants and complex diseases. However, common SNPs detected by current GWAS only explain a small proportion of heritable variability. With the development of next-generation sequencing technologies, researchers find more and more evidence to support the role played by rare variants in heritable variability. However, rare and common variants are often studied separately. The objective of this paper is to develop a robust strategy to analyze association between complex traits and genetic regions using both common and rare variants. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 43% |
| Student > Ph. D. Student | 4 | 19% |
| Professor > Associate Professor | 2 | 10% |
| Professor | 1 | 5% |
| Student > Doctoral Student | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 52% |
| Biochemistry, Genetics and Molecular Biology | 6 | 29% |
| Social Sciences | 1 | 5% |
| Unknown | 3 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 February 2012.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from BMC Genomic Data
#786
of 1,204 outputs
Outputs of similar age
#200,388
of 253,903 outputs
Outputs of similar age from BMC Genomic Data
#12
of 18 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 28th percentile – i.e., 28% 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 253,903 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.