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Simulating a base population in honey bee for molecular genetic studies

Overview of attention for article published in Genetics Selection Evolution, June 2012
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Title
Simulating a base population in honey bee for molecular genetic studies
Published in
Genetics Selection Evolution, June 2012
DOI 10.1186/1297-9686-44-14
Pubmed ID
Authors

Pooja Gupta, Tim Conrad, Andreas Spötter, Norbert Reinsch, Kaspar Bienefeld

Abstract

Over the past years, reports have indicated that honey bee populations are declining and that infestation by an ecto-parasitic mite (Varroa destructor) is one of the main causes. Selective breeding of resistant bees can help to prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Genomic selection has emerged as an important tool in animal breeding programs and simulation studies have shown that it yields more accurate breeding value estimates, higher genetic gain and low rates of inbreeding. Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented. Although genomic datasets have been simulated in other species undergoing genetic evaluation, simulation of a genomic dataset specific to the honey bee is required since this species has a distinct genetic and reproductive biology. Our software program was aimed at constructing a base population by simulating a random mating honey bee population. A forward-time population simulation approach was applied since it allows modeling of genetic characteristics and reproductive behavior specific to the honey bee.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
New Zealand 1 2%
Brazil 1 2%
Unknown 49 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 30%
Student > Ph. D. Student 11 21%
Student > Master 6 11%
Student > Bachelor 4 8%
Other 4 8%
Other 9 17%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 39 74%
Environmental Science 4 8%
Biochemistry, Genetics and Molecular Biology 4 8%
Veterinary Science and Veterinary Medicine 1 2%
Arts and Humanities 1 2%
Other 2 4%
Unknown 2 4%
Attention Score in Context

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 03 March 2013.
All research outputs
#22,759,802
of 25,374,647 outputs
Outputs from Genetics Selection Evolution
#773
of 822 outputs
Outputs of similar age
#160,618
of 177,507 outputs
Outputs of similar age from Genetics Selection Evolution
#3
of 3 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 1st percentile – i.e., 1% 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 177,507 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
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