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Attention Score in Context
| Title |
Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken
|
|---|---|
| Published in |
BMC Genomics, November 2011
|
| DOI | 10.1186/1471-2164-12-567 |
| Pubmed ID | |
| Authors |
Yuna Blum, Guillaume Le Mignon, David Causeur, Olivier Filangi, Colette Désert, Olivier Demeure, Pascale Le Roy, Sandrine Lagarrigue |
| Abstract |
Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 33% |
| United Kingdom | 1 | 33% |
| Australia | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 5% |
| Romania | 1 | 5% |
| Unknown | 20 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 32% |
| Researcher | 6 | 27% |
| Professor | 3 | 14% |
| Student > Master | 2 | 9% |
| Student > Bachelor | 2 | 9% |
| Other | 0 | 0% |
| Unknown | 2 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 55% |
| Biochemistry, Genetics and Molecular Biology | 3 | 14% |
| Computer Science | 1 | 5% |
| Economics, Econometrics and Finance | 1 | 5% |
| Medicine and Dentistry | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 4 | 18% |
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 10 January 2016.
All research outputs
#14,722,660
of 22,659,164 outputs
Outputs from BMC Genomics
#6,105
of 10,607 outputs
Outputs of similar age
#158,290
of 239,286 outputs
Outputs of similar age from BMC Genomics
#169
of 304 outputs
Altmetric has tracked 22,659,164 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,607 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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We're also able to compare this research output to 304 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.