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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Assessing the value of phenotypic information from non-genotyped animals for QTL mapping of complex traits in real and simulated populations
|
|---|---|
| Published in |
BMC Genomic Data, June 2016
|
| DOI | 10.1186/s12863-016-0394-1 |
| Pubmed ID | |
| Authors |
Thaise P. Melo, Luciana Takada, Fernando Baldi, Henrique N. Oliveira, Marina M. Dias, Haroldo H. R. Neves, Flavio S. Schenkel, Lucia G. Albuquerque, Roberto Carvalheiro |
| Abstract |
QTL mapping through genome-wide association studies (GWAS) is challenging, especially in the case of low heritability complex traits and when few animals possess genotypic and phenotypic information. When most of the phenotypic information is from non-genotyped animals, GWAS can be performed using the weighted single-step GBLUP (WssGBLUP) method, which permits to combine all available information, even that of non-genotyped animals. However, it is not clear to what extent phenotypic information from non-genotyped animals increases the power of QTL detection, and whether factors such as the extent of linkage disequilibrium (LD) in the population and weighting SNPs in WssGBLUP affect the importance of using information from non-genotyped animals in GWAS. These questions were investigated in this study using real and simulated data. Analysis of real data showed that the use of phenotypes of non-genotyped animals affected SNP effect estimates and, consequently, QTL mapping. Despite some coincidence, the most important genomic regions identified by the analyses, either using or ignoring phenotypes of non-genotyped animals, were not the same. The simulation results indicated that the inclusion of all available phenotypic information, even that of non-genotyped animals, tends to improve QTL detection for low heritability complex traits. For populations with low levels of LD, this trend of improvement was less pronounced. Stronger shrinkage on SNPs explaining lower variance was not necessarily associated with better QTL mapping. The use of phenotypic information from non-genotyped animals in GWAS may improve the ability to detect QTL for low heritability complex traits, especially in populations in which the level of LD is high. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 1 | 3% |
| Unknown | 39 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 18% |
| Researcher | 7 | 18% |
| Other | 3 | 8% |
| Student > Doctoral Student | 3 | 8% |
| Student > Master | 3 | 8% |
| Other | 9 | 23% |
| Unknown | 8 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 53% |
| Biochemistry, Genetics and Molecular Biology | 5 | 13% |
| Medicine and Dentistry | 2 | 5% |
| Unspecified | 1 | 3% |
| Veterinary Science and Veterinary Medicine | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 8 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 June 2016.
All research outputs
#15,168,964
of 25,373,627 outputs
Outputs from BMC Genomic Data
#480
of 1,204 outputs
Outputs of similar age
#201,816
of 369,274 outputs
Outputs of similar age from BMC Genomic Data
#9
of 47 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 58% 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 369,274 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 47 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 74% of its contemporaries.