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Attention Score in Context
| Title |
Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in rice
|
|---|---|
| Published in |
BMC Genomic Data, April 2014
|
| DOI | 10.1186/1471-2156-15-50 |
| Pubmed ID | |
| Authors |
Eiji Yamamoto, Hiroyoshi Iwata, Takanari Tanabata, Ritsuko Mizobuchi, Jun-ichi Yonemaru, Toshio Yamamoto, Masahiro Yano |
| Abstract |
In genetic analysis of agronomic traits, quantitative trait loci (QTLs) that control the same phenotype are often closely linked. Furthermore, many QTLs are localized in specific genomic regions (QTL clusters) that include naturally occurring allelic variations in different genes. Therefore, linkage among QTLs may complicate the detection of each individual QTL. This problem can be resolved by using populations that include many potential recombination sites. Recently, multi-parent populations have been developed and used for QTL analysis. However, their efficiency for detection of linked QTLs has not received attention. By using information on rice, we simulated the construction of a multi-parent population followed by cycles of recurrent crossing and inbreeding, and we investigated the resulting genome structure and its usefulness for detecting linked QTLs as a function of the number of cycles of recurrent crossing. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | 3% |
| Netherlands | 1 | 3% |
| Unknown | 35 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 27% |
| Researcher | 9 | 24% |
| Professor > Associate Professor | 4 | 11% |
| Student > Master | 4 | 11% |
| Student > Doctoral Student | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 5 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 26 | 70% |
| Computer Science | 2 | 5% |
| Nursing and Health Professions | 1 | 3% |
| Social Sciences | 1 | 3% |
| Unknown | 7 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 27 April 2014.
All research outputs
#20,656,161
of 25,374,647 outputs
Outputs from BMC Genomic Data
#861
of 1,204 outputs
Outputs of similar age
#177,390
of 241,070 outputs
Outputs of similar age from BMC Genomic Data
#13
of 22 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% 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 is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.