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Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Overview of attention for article published in Theoretical and Applied Genetics, November 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 (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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142 Mendeley
Title
Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials
Published in
Theoretical and Applied Genetics, November 2016
DOI 10.1007/s00122-016-2818-8
Pubmed ID
Authors

Sebastian Michel, Christian Ametz, Huseyin Gungor, Batuhan Akgöl, Doru Epure, Heinrich Grausgruber, Franziska Löschenberger, Hermann Buerstmayr

Abstract

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 <1%
Belgium 1 <1%
Unknown 140 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 24%
Researcher 30 21%
Student > Master 16 11%
Student > Bachelor 7 5%
Student > Doctoral Student 6 4%
Other 16 11%
Unknown 33 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 94 66%
Biochemistry, Genetics and Molecular Biology 6 4%
Computer Science 2 1%
Mathematics 1 <1%
Social Sciences 1 <1%
Other 0 0%
Unknown 38 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 16 April 2017.
All research outputs
#4,164,375
of 23,794,258 outputs
Outputs from Theoretical and Applied Genetics
#563
of 3,565 outputs
Outputs of similar age
#66,927
of 315,224 outputs
Outputs of similar age from Theoretical and Applied Genetics
#12
of 52 outputs
Altmetric has tracked 23,794,258 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,565 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 84% 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 315,224 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 78% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.