Literature DB >> 26566829

Genomic selection in maritime pine.

Fikret Isik1, Jérôme Bartholomé2, Alfredo Farjat3, Emilie Chancerel2, Annie Raffin2, Leopoldo Sanchez4, Christophe Plomion2, Laurent Bouffier5.   

Abstract

A two-generation maritime pine (Pinus pinaster Ait.) breeding population (n=661) was genotyped using 2500 SNP markers. The extent of linkage disequilibrium and utility of genomic selection for growth and stem straightness improvement were investigated. The overall intra-chromosomal linkage disequilibrium was r(2)=0.01. Linkage disequilibrium corrected for genomic relationships derived from markers was smaller (rV(2)=0.006). Genomic BLUP, Bayesian ridge regression and Bayesian LASSO regression statistical models were used to obtain genomic estimated breeding values. Two validation methods (random sampling 50% of the population and 10% of the progeny generation as validation sets) were used with 100 replications. The average predictive ability across statistical models and validation methods was about 0.49 for stem sweep, and 0.47 and 0.43 for total height and tree diameter, respectively. The sensitivity analysis suggested that prior densities (variance explained by markers) had little or no discernible effect on posterior means (residual variance) in Bayesian prediction models. Sampling from the progeny generation for model validation increased the predictive ability of markers for tree diameter and stem sweep but not for total height. The results are promising despite low linkage disequilibrium and low marker coverage of the genome (∼1.39 markers/cM).
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Bayesian regression; Genomic relationship; Linkage disequilibrium; Pinus pinaster; Tree breeding

Mesh:

Substances:

Year:  2015        PMID: 26566829     DOI: 10.1016/j.plantsci.2015.08.006

Source DB:  PubMed          Journal:  Plant Sci        ISSN: 0168-9452            Impact factor:   4.729


  28 in total

Review 1.  Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.

Authors:  C Anilkumar; N C Sunitha; Narayana Bhat Devate; S Ramesh
Journal:  Planta       Date:  2022-09-23       Impact factor: 4.540

2.  Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model.

Authors:  R T Resende; M D V Resende; F F Silva; C F Azevedo; E K Takahashi; O B Silva-Junior; D Grattapaglia
Journal:  Heredity (Edinb)       Date:  2017-07-05       Impact factor: 3.821

3.  Single-step genomic prediction of Eucalyptus dunnii using different identity-by-descent and identity-by-state relationship matrices.

Authors:  Esteban J Jurcic; Pamela V Villalba; Pablo S Pathauer; Dino A Palazzini; Gustavo P J Oberschelp; Leonel Harrand; Martín N Garcia; Natalia C Aguirre; Cintia V Acuña; María C Martínez; Juan G Rivas; Esteban F Cisneros; Juan A López; Susana N Marcucci Poltri; Sebastián Munilla; Eduardo P Cappa
Journal:  Heredity (Edinb)       Date:  2021-06-18       Impact factor: 3.832

4.  Prediction ability of genome-wide markers in Pinus taeda L. within and between population is affected by relatedness to the training population and trait genetic architecture.

Authors:  Edwin Lauer; James Holland; Fikret Isik
Journal:  G3 (Bethesda)       Date:  2022-02-04       Impact factor: 3.542

5.  Linkage and Association Mapping for Two Major Traits Used in the Maritime Pine Breeding Program: Height Growth and Stem Straightness.

Authors:  Jérôme Bartholomé; Marco Cam Bink; Joost van Heerwaarden; Emilie Chancerel; Christophe Boury; Isabelle Lesur; Fikret Isik; Laurent Bouffier; Christophe Plomion
Journal:  PLoS One       Date:  2016-11-02       Impact factor: 3.240

6.  Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.

Authors:  Bárbara S F Müller; Leandro G Neves; Janeo E de Almeida Filho; Márcio F R Resende; Patricio R Muñoz; Paulo E T Dos Santos; Estefano Paludzyszyn Filho; Matias Kirst; Dario Grattapaglia
Journal:  BMC Genomics       Date:  2017-07-11       Impact factor: 3.969

7.  Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana).

Authors:  Patrick R N Lenz; Jean Beaulieu; Shawn D Mansfield; Sébastien Clément; Mireille Desponts; Jean Bousquet
Journal:  BMC Genomics       Date:  2017-04-28       Impact factor: 3.969

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

Authors:  Biyue Tan; Dario Grattapaglia; Gustavo Salgado Martins; Karina Zamprogno Ferreira; Björn Sundberg; Pär K Ingvarsson
Journal:  BMC Plant Biol       Date:  2017-06-29       Impact factor: 4.215

9.  Performance of genomic prediction within and across generations in maritime pine.

Authors:  Jérôme Bartholomé; Joost Van Heerwaarden; Fikret Isik; Christophe Boury; Marjorie Vidal; Christophe Plomion; Laurent Bouffier
Journal:  BMC Genomics       Date:  2016-08-11       Impact factor: 3.969

10.  Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform.

Authors:  Frances R Thistlethwaite; Blaise Ratcliffe; Jaroslav Klápště; Ilga Porth; Charles Chen; Michael U Stoehr; Yousry A El-Kassaby
Journal:  BMC Genomics       Date:  2017-12-02       Impact factor: 3.969

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