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Attention Score in Context
| Title |
A robust and efficient statistical method for genetic association studies using case and control samples from multiple cohorts
|
|---|---|
| Published in |
BMC Genomics, February 2013
|
| DOI | 10.1186/1471-2164-14-88 |
| Pubmed ID | |
| Authors |
Minghui Wang, Lin Wang, Ning Jiang, Tianye Jia, Zewei Luo |
| Abstract |
The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case-control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| United States | 1 | 4% |
| Germany | 1 | 4% |
| Unknown | 24 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 33% |
| Researcher | 7 | 26% |
| Student > Doctoral Student | 2 | 7% |
| Student > Master | 2 | 7% |
| Lecturer > Senior Lecturer | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 4 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 41% |
| Biochemistry, Genetics and Molecular Biology | 4 | 15% |
| Medicine and Dentistry | 2 | 7% |
| Psychology | 2 | 7% |
| Arts and Humanities | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 5 | 19% |
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 11 February 2013.
All research outputs
#21,264,673
of 23,881,329 outputs
Outputs from BMC Genomics
#9,455
of 10,793 outputs
Outputs of similar age
#256,669
of 289,623 outputs
Outputs of similar age from BMC Genomics
#318
of 357 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,793 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% 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 289,623 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 357 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.