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Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

Overview of attention for article published in BMC Genomics, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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Title
Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations
Published in
BMC Genomics, January 2016
DOI 10.1186/s12864-015-2345-z
Pubmed ID
Authors

Gregor Gorjanc, Janez Jenko, Sarah J. Hearne, John M. Hickey

Abstract

The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program. The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives. Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 200 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 2 1%
Chile 1 <1%
France 1 <1%
Germany 1 <1%
Brazil 1 <1%
Italy 1 <1%
Belgium 1 <1%
Denmark 1 <1%
Unknown 191 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 48 24%
Student > Master 39 20%
Student > Ph. D. Student 38 19%
Student > Bachelor 10 5%
Student > Doctoral Student 10 5%
Other 21 11%
Unknown 34 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 125 63%
Biochemistry, Genetics and Molecular Biology 11 6%
Social Sciences 4 2%
Computer Science 3 2%
Mathematics 3 2%
Other 13 7%
Unknown 41 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 13 January 2016.
All research outputs
#5,529,389
of 22,837,982 outputs
Outputs from BMC Genomics
#2,230
of 10,655 outputs
Outputs of similar age
#87,921
of 393,343 outputs
Outputs of similar age from BMC Genomics
#43
of 264 outputs
Altmetric has tracked 22,837,982 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 79% 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 393,343 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 264 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.