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Genotyping by sequencing for genomic prediction in a soybean breeding population

Overview of attention for article published in BMC Genomics, August 2014
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  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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3 X users

Citations

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187 Dimensions

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285 Mendeley
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Title
Genotyping by sequencing for genomic prediction in a soybean breeding population
Published in
BMC Genomics, August 2014
DOI 10.1186/1471-2164-15-740
Pubmed ID
Authors

Diego Jarquín, Kyle Kocak, Luis Posadas, Katie Hyma, Joseph Jedlicka, George Graef, Aaron Lorenz

Abstract

Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 285 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 1%
United States 2 <1%
Italy 1 <1%
Ghana 1 <1%
France 1 <1%
Australia 1 <1%
Germany 1 <1%
Denmark 1 <1%
Belgium 1 <1%
Other 0 0%
Unknown 273 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 24%
Researcher 56 20%
Student > Master 37 13%
Student > Bachelor 21 7%
Student > Doctoral Student 21 7%
Other 36 13%
Unknown 45 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 185 65%
Biochemistry, Genetics and Molecular Biology 22 8%
Mathematics 6 2%
Computer Science 4 1%
Social Sciences 3 1%
Other 10 4%
Unknown 55 19%
Attention Score in Context

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 31 August 2014.
All research outputs
#13,716,686
of 22,761,738 outputs
Outputs from BMC Genomics
#5,282
of 10,638 outputs
Outputs of similar age
#115,502
of 236,210 outputs
Outputs of similar age from BMC Genomics
#79
of 181 outputs
Altmetric has tracked 22,761,738 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 10,638 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 236,210 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 50% of its contemporaries.
We're also able to compare this research output to 181 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 56% of its contemporaries.