You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Data analysis in the post‐genome‐wide association study era
|
|---|---|
| Published in |
Chronic Diseases and Translational Medicine, December 2016
|
| DOI | 10.1016/j.cdtm.2016.11.009 |
| Pubmed ID | |
| Authors |
Qiao‐Ling Wang, Wen‐Le Tan, Yan‐Jie Zhao, Ming‐Ming Shao, Jia‐Hui Chu, Xu‐Dong Huang, Jun Li, Ying‐Ying Luo, Lin‐Na Peng, Qiong‐Hua Cui, Ting Feng, Jie Yang, Ya‐Ling Han |
| Abstract |
Since the first report of a genome-wide association study (GWAS) on human age-related macular degeneration, GWAS has successfully been used to discover genetic variants for a variety of complex human diseases and/or traits, and thousands of associated loci have been identified. However, the underlying mechanisms for these loci remain largely unknown. To make these GWAS findings more useful, it is necessary to perform in-depth data mining. The data analysis in the post-GWAS era will include the following aspects: fine-mapping of susceptibility regions to identify susceptibility genes for elucidating the biological mechanism of action; joint analysis of susceptibility genes in different diseases; integration of GWAS, transcriptome, and epigenetic data to analyze expression and methylation quantitative trait loci at the whole-genome level, and find single-nucleotide polymorphisms that influence gene expression and DNA methylation; genome-wide association analysis of disease-related DNA copy number variations. Applying these strategies and methods will serve to strengthen GWAS data to enhance the utility and significance of GWAS in improving understanding of the genetics of complex diseases or traits and translate these findings for clinical applications. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Thailand | 1 | 2% |
| Unknown | 44 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 24% |
| Student > Doctoral Student | 6 | 13% |
| Researcher | 5 | 11% |
| Student > Master | 4 | 9% |
| Lecturer | 3 | 7% |
| Other | 10 | 22% |
| Unknown | 6 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 29% |
| Agricultural and Biological Sciences | 9 | 20% |
| Medicine and Dentistry | 8 | 18% |
| Computer Science | 8 | 18% |
| Unspecified | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 4 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 25 October 2017.
All research outputs
#3,025,186
of 25,373,627 outputs
Outputs from Chronic Diseases and Translational Medicine
#20
of 209 outputs
Outputs of similar age
#55,630
of 416,449 outputs
Outputs of similar age from Chronic Diseases and Translational Medicine
#2
of 9 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 209 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has done particularly well, scoring higher than 90% 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 416,449 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 86% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.