title: Performance of genomic prediction within and across generations in maritime pine.
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.
authors: BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France.
authors: Jérôme
authors: 0000-0002-0855-3828
authors: J
authors: Bartholomé
authors: Biometris, Wageningen University and Research Centre, NL-6700 AC, Wageningen, The Netherlands.
authors: Joost
authors: J
authors: Van Heerwaarden
authors: Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA.
authors: Fikret
authors: F
authors: Isik
authors: BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France.
authors: Christophe
authors: C
authors: Boury
authors: BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France.|FCBA, Biotechnology and Advanced Silviculture Department, Genetics & Biotechnology Team, 33610, Cestas, France.
authors: Marjorie
authors: M
authors: Vidal
authors: BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France.
authors: Christophe
authors: C
authors: Plomion
authors: BIOGECO, INRA, Univ. Bordeaux, 33610, Cestas, France. bouffier@pierroton.inra.fr.
authors: Laurent
authors: L
authors: Bouffier
mesh_terms: D001499:Bayes Theorem; D005819:Genetic Markers; D018745:Genome, Plant; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D028223:Pinus / Q000235:genetics*; D000069600:Plant Breeding / Q000706:statistics & numerical data*; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D012641:Selection, Genetic
chemical_list: D005819:Genetic Markers
keywords: Genomic selection; Growth; Multiple generations; Pinus pinaster; Progeny validation; Relatedness; Stem straightness