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Mendeley readers
Attention Score in Context
| Title |
A novel approach for multi-SNP GWAS and its application in Alzheimer’s disease
|
|---|---|
| Published in |
BMC Bioinformatics, July 2016
|
| DOI | 10.1186/s12859-016-1093-7 |
| Pubmed ID | |
| Authors |
Paul M. Bodily, M. Stanley Fujimoto, Justin T. Page, Mark J. Clement, Mark T. W. Ebbert, Perry G. Ridge, the Alzheimer’s Disease Neuroimaging Initiative |
| Abstract |
Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype. Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory. |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 2% |
| Italy | 1 | 2% |
| Unknown | 48 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 24% |
| Student > Master | 8 | 16% |
| Researcher | 6 | 12% |
| Student > Bachelor | 3 | 6% |
| Professor | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 11 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 24% |
| Neuroscience | 8 | 16% |
| Biochemistry, Genetics and Molecular Biology | 7 | 14% |
| Medicine and Dentistry | 4 | 8% |
| Computer Science | 3 | 6% |
| Other | 4 | 8% |
| Unknown | 12 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 30 July 2016.
All research outputs
#4,191,741
of 22,881,964 outputs
Outputs from BMC Bioinformatics
#1,617
of 7,298 outputs
Outputs of similar age
#76,432
of 365,443 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 99 outputs
Altmetric has tracked 22,881,964 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,298 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 77% 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 365,443 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 77% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.