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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle
|
|---|---|
| Published in |
Genetics Selection Evolution, July 2014
|
| DOI | 10.1186/1297-9686-46-41 |
| Pubmed ID | |
| Authors |
Rianne van Binsbergen, Marco CAM Bink, Mario PL Calus, Fred A van Eeuwijk, Ben J Hayes, Ina Hulsegge, Roel F Veerkamp |
| Abstract |
The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 3 | 50% |
| United Kingdom | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 1% |
| United States | 2 | 1% |
| Netherlands | 1 | <1% |
| Colombia | 1 | <1% |
| Belgium | 1 | <1% |
| New Zealand | 1 | <1% |
| Unknown | 137 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 21% |
| Researcher | 28 | 19% |
| Student > Master | 19 | 13% |
| Student > Doctoral Student | 13 | 9% |
| Other | 6 | 4% |
| Other | 15 | 10% |
| Unknown | 34 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 88 | 61% |
| Biochemistry, Genetics and Molecular Biology | 9 | 6% |
| Veterinary Science and Veterinary Medicine | 6 | 4% |
| Engineering | 3 | 2% |
| Environmental Science | 1 | <1% |
| Other | 4 | 3% |
| Unknown | 34 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 17 July 2014.
All research outputs
#7,263,731
of 25,373,627 outputs
Outputs from Genetics Selection Evolution
#236
of 822 outputs
Outputs of similar age
#65,313
of 241,649 outputs
Outputs of similar age from Genetics Selection Evolution
#1
of 6 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 70% 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 241,649 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them