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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genomic prediction of traits related to canine hip dysplasia
|
|---|---|
| Published in |
Frontiers in Genetics, March 2015
|
| DOI | 10.3389/fgene.2015.00097 |
| Pubmed ID | |
| Authors |
Enrique Sánchez-Molano, Ricardo Pong-Wong, Dylan N. Clements, Sarah C. Blott, Pamela Wiener, John A. Woolliams |
| Abstract |
Increased concern for the welfare of pedigree dogs has led to development of selection programs against inherited diseases. An example is canine hip dysplasia (CHD), which has a moderate heritability and a high prevalence in some large-sized breeds. To date, selection using phenotypes has led to only modest improvement, and alternative strategies such as genomic selection (GS) may prove more effective. The primary aims of this study were to compare the performance of pedigree- and genomic-based breeding against CHD in the UK Labrador retriever population and to evaluate the performance of different GS methods. A sample of 1179 Labrador Retrievers evaluated for CHD according to the UK scoring method (hip score, HS) was genotyped with the Illumina CanineHD BeadChip. Twelve functions of HS and its component traits were analyzed using different statistical methods (GBLUP, Bayes C and Single-Step methods), and results were compared with a pedigree-based approach (BLUP) using cross-validation. Genomic methods resulted in similar or higher accuracies than pedigree-based methods with training sets of 944 individuals for all but the untransformed HS, suggesting that GS is an effective strategy. GBLUP and Bayes C gave similar prediction accuracies for HS and related traits, indicating a polygenic architecture. This conclusion was also supported by the low accuracies obtained in additional GBLUP analyses performed using only the SNPs with highest test statistics, also indicating that marker-assisted selection (MAS) would not be as effective as GS. A Single-Step method that combines genomic and pedigree information also showed higher accuracy than GBLUP and Bayes C for the log-transformed HS, which is currently used for pedigree based evaluations in UK. In conclusion, GS is a promising alternative to pedigree-based selection against CHD, requiring more phenotypes with genomic data to improve further the accuracy of prediction. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 20% |
| India | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Poland | 1 | 1% |
| Belgium | 1 | 1% |
| Unknown | 65 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 13 | 19% |
| Student > Master | 12 | 18% |
| Researcher | 10 | 15% |
| Other | 7 | 10% |
| Student > Doctoral Student | 6 | 9% |
| Other | 8 | 12% |
| Unknown | 11 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 36% |
| Veterinary Science and Veterinary Medicine | 16 | 24% |
| Biochemistry, Genetics and Molecular Biology | 6 | 9% |
| Medicine and Dentistry | 4 | 6% |
| Social Sciences | 3 | 4% |
| Other | 2 | 3% |
| Unknown | 12 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 31 March 2015.
All research outputs
#12,725,419
of 22,794,367 outputs
Outputs from Frontiers in Genetics
#2,568
of 11,761 outputs
Outputs of similar age
#116,159
of 260,871 outputs
Outputs of similar age from Frontiers in Genetics
#71
of 154 outputs
Altmetric has tracked 22,794,367 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,761 research outputs from this source. They receive a mean Attention Score of 3.7. 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 260,871 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 54% of its contemporaries.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.