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Attention Score in Context
| Title |
Genetic polymorphisms of pharmacogenomic VIP variants in the Uygur population from northwestern China
|
|---|---|
| Published in |
BMC Genomic Data, June 2015
|
| DOI | 10.1186/s12863-015-0232-x |
| Pubmed ID | |
| Authors |
Li Wang, Ainiwaer Aikemu, Ayiguli Yibulayin, Shuli Du, Tingting Geng, Bo Wang, Yuan Zhang, Tianbo Jin, Jie Yang |
| Abstract |
Drug response variability observed amongst patients is caused by the interaction of both genetic and non-genetic factors, and frequencies of functional genetic variants are known to vary amongst populations. Pharmacogenomic research has the potential to help with individualized treatments. We have not found any pharmacogenomics information regarding Uygur ethnic group in northwest China. In the present study, we genotyped 85 very important pharmacogenetic (VIP) variants (selected from the PharmGKB database) in the Uygur population and compared our data with other eleven populations from the HapMap data set. Through statistical analysis, we found that CYP3A5 rs776746, VKORC1 rs9934438, and VKORC1 rs7294 were most different in Uygur compared with most of the eleven populations from the HapMap data set. Compared with East Asia populations, allele A of rs776746 is less frequent and allele A of rs7294 is more frequent in the Uygur population. The analysis of F-statistics (Fst) and population structure shows that the genetic background of Uygur is relatively close to that of MEX. Our results show significant differences amongst Chinese populations that will help clinicians triage patients for better individualized treatments. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 22% |
| Student > Ph. D. Student | 2 | 22% |
| Student > Doctoral Student | 1 | 11% |
| Other | 1 | 11% |
| Professor | 1 | 11% |
| Other | 0 | 0% |
| Unknown | 2 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 22% |
| Agricultural and Biological Sciences | 2 | 22% |
| Medicine and Dentistry | 2 | 22% |
| Unknown | 3 | 33% |
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 20 June 2015.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from BMC Genomic Data
#1,008
of 1,204 outputs
Outputs of similar age
#237,259
of 278,563 outputs
Outputs of similar age from BMC Genomic Data
#35
of 42 outputs
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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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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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 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.