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Performance of genomic prediction within and across generations in maritime pine

Overview of attention for article published in BMC Genomics, August 2016
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Title
Performance of genomic prediction within and across generations in maritime pine
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2879-8
Pubmed ID
Authors

Jérôme Bartholomé, Joost Van Heerwaarden, Fikret Isik, Christophe Boury, Marjorie Vidal, Christophe Plomion, Laurent Bouffier

Abstract

Genomic selection (GS) is a promising approach for decreasing breeding cycle length in forest trees. Assessment of progeny performance and of the prediction accuracy of GS models over generations is therefore a key issue. A reference population of maritime pine (Pinus pinaster) with an estimated effective inbreeding population size (status number) of 25 was first selected with simulated data. This reference population (n = 818) covered three generations (G0, G1 and G2) and was genotyped with 4436 single-nucleotide polymorphism (SNP) markers. We evaluated the effects on prediction accuracy of both the relatedness between the calibration and validation sets and validation on the basis of progeny performance. Pedigree-based (best linear unbiased prediction, ABLUP) and marker-based (genomic BLUP and Bayesian LASSO) models were used to predict breeding values for three different traits: circumference, height and stem straightness. On average, the ABLUP model outperformed genomic prediction models, with a maximum difference in prediction accuracies of 0.12, depending on the trait and the validation method. A mean difference in prediction accuracy of 0.17 was found between validation methods differing in terms of relatedness. Including the progenitors in the calibration set reduced this difference in prediction accuracy to 0.03. When only genotypes from the G0 and G1 generations were used in the calibration set and genotypes from G2 were used in the validation set (progeny validation), prediction accuracies ranged from 0.70 to 0.85. This study suggests that the training of prediction models on parental populations can predict the genetic merit of the progeny with high accuracy: an encouraging result for the implementation of GS in the maritime pine breeding program.

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Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Netherlands 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 33%
Student > Ph. D. Student 10 20%
Unspecified 6 12%
Student > Doctoral Student 5 10%
Student > Postgraduate 4 8%
Other 9 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 73%
Unspecified 9 18%
Biochemistry, Genetics and Molecular Biology 3 6%
Business, Management and Accounting 1 2%
Environmental Science 1 2%
Other 0 0%

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 18 August 2016.
All research outputs
#4,382,047
of 8,243,836 outputs
Outputs from BMC Genomics
#3,721
of 5,827 outputs
Outputs of similar age
#129,093
of 233,740 outputs
Outputs of similar age from BMC Genomics
#169
of 264 outputs
Altmetric has tracked 8,243,836 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
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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 is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.