Performance of genomic prediction within and across generations in maritime pine

BMC Genomics

Research Article

Christophe Boury¹  Christophe Plomion¹  Jérôme Bartholomé¹  Laurent Bouffier¹  Marjorie Vidal²  Joost Van Heerwaarden³  Fikret Isik⁴ 

[1] BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France;BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France;FCBA, Biotechnology and Advanced Silviculture Department, Genetics & Biotechnology Team, 33610, Cestas, France;Biometris, Wageningen University and Research Centre, NL-6700 AC, Wageningen, The Netherlands;Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA;

关键词: Genomic selection;    Growth;    Multiple generations;    Pinus pinaster;    Progeny validation;    Relatedness;    Stem straightness;   

DOI  :  10.1186/s12864-016-2879-8

 received in 2016-03-18, accepted in 2016-07-05,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundGenomic 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.ResultsA 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.ConclusionsThis 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.

【 授权许可】

CC BY   
© The Author(s). 2016

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