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Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids

Overview of attention for article published in BMC Plant Biology, June 2017
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

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8 tweeters

Citations

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Title
Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids
Published in
BMC Plant Biology, June 2017
DOI 10.1186/s12870-017-1059-6
Pubmed ID
Authors

Biyue Tan, Dario Grattapaglia, Gustavo Salgado Martins, Karina Zamprogno Ferreira, Björn Sundberg, Pär K. Ingvarsson

Abstract

Genomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F1 hybrids to develop genomic prediction models for eight phenotypic traits - basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA). Heritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, the GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the true proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in F1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore either GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions. The size and composition of the training set and number of SNPs used are the two most important factors for model prediction, compared to the statistical methods and the genomic location of SNPs. Furthermore, training the prediction model based on pure parental species only provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in Eucalyptus breeding programs by the selection of interspecific hybrids.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 1%
Unknown 97 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 19%
Student > Master 16 16%
Researcher 12 12%
Student > Doctoral Student 11 11%
Student > Bachelor 7 7%
Other 16 16%
Unknown 17 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 59%
Biochemistry, Genetics and Molecular Biology 12 12%
Business, Management and Accounting 2 2%
Environmental Science 2 2%
Engineering 1 1%
Other 1 1%
Unknown 22 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 01 August 2017.
All research outputs
#6,412,118
of 20,419,783 outputs
Outputs from BMC Plant Biology
#535
of 2,875 outputs
Outputs of similar age
#101,552
of 283,026 outputs
Outputs of similar age from BMC Plant Biology
#1
of 1 outputs
Altmetric has tracked 20,419,783 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 2,875 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 80% 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 283,026 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 63% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them