scholarly journals Multi‐trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce

2019 ◽  
Vol 13 (1) ◽  
pp. 76-94 ◽  
Author(s):  
Patrick R. N. Lenz ◽  
Simon Nadeau ◽  
Marie‐Josée Mottet ◽  
Martin Perron ◽  
Nathalie Isabel ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Norway Spruce ◽  
Wood Quality ◽  
Selection For

scholarly journals Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits

2020 ◽  
Vol 13 (10) ◽  
pp. 2704-2722 ◽  
Author(s):  
Jean Beaulieu ◽  
Simon Nadeau ◽  
Chen Ding ◽  
Jose M. Celedon ◽  
Aïda Azaiez ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Wood Quality ◽  
Spruce Budworm ◽  
White Spruce ◽  
Quality Traits ◽  
Selection For

scholarly journals Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees

2012 ◽  
Vol 194 (1) ◽  
pp. 116-128 ◽  
Author(s):  
Marcos D. V. Resende ◽  
Márcio F. R. Resende ◽  
Carolina P. Sansaloni ◽  
Cesar D. Petroli ◽  
Alexandre A. Missiaggia ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Wood Quality ◽  
Forest Trees ◽  
Missing Heritability ◽  
Selection For

scholarly journals Increased prediction ability in Norway spruce trials using a marker x environment interaction and non-additive genomic selection model

2018 ◽  
Author(s):  
Zhi-Qiang Chen ◽  
John Baison ◽  
Jin Pan ◽  
Johan Westin ◽  
María Rosario García Gil ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Norway Spruce ◽  
Wood Quality ◽  
Genetic Parameter ◽  
Tree Height ◽  
Phenotypic Selection ◽  
Exome Capture ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Prediction Ability

AbstractA genomic selection (GS) study of growth and wood quality traits is reported based on control-pollinated Norway spruce families established in two Northern Swedish trials at two locations using exome capture as a genotyping platform. Non-additive effects including dominance and first-order epistatic interactions (including additive by additive, dominance by dominance, and additive by dominance) and marker-by-environment interaction (M×E) effects were dissected in genomic and phenotypic selection models. GS models partitioned additive and non-additive genetic variances more precisely compared with pedigree-based models. In addition, predictive ability (PA) in GS was substantially increased by including dominance and slightly increased by including M×E effects when these effects are significant. For velocity, response to GS (RGS) per year increased 91.3/43.7%, 86.9/82.9%, and 78.9/80.8% compared with response to phenotypic selection (RPS) per year when GS was based on 1) main marker effects (M), 2) M + M×E effects (A), and 3) A + dominance effects (AD) for site 1/site 2, respectively. This indicates that including M×E and dominance effects not only improves genetic parameter estimates but also may improve the genetic gain when they are significant. For tree height, Pilodyn, and modulus of elasticity (MOE), RGS per year improved up to 84.2%, 91.3%, and 92.6% compared with RPS per year, respectively.

Expected benefits of genomic selection for growth and wood quality traits in Eucalyptus grandis

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Makobatjatji M. Mphahlele ◽  
Fikret Isik ◽  
Marja M. Mostert-O’Neill ◽  
S. Melissa Reynolds ◽  
Gary R. Hodge ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Wood Quality ◽  
Eucalyptus Grandis ◽  
Quality Traits ◽  
Selection For ◽  
Expected Benefits

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

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Patrick R.N. Lenz ◽  
Jean Beaulieu ◽  
Shawn D. Mansfield ◽  
Sébastien Clément ◽  
Mireille Desponts ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Picea Mariana ◽  
Black Spruce ◽  
Wood Quality ◽  
Breeding Population ◽  
Quality Traits ◽  
Factors Affecting ◽  
Selection For

scholarly journals Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as genotyping platform in Norway spruce

2018 ◽  
Author(s):  
Zhi-Qiang Chen ◽  
John Baison ◽  
Jin Pan ◽  
Bo Karlsson ◽  
Bengt Andersson Gull ◽  
...  
Keyword(s):  
Family Structure ◽  
Genomic Selection ◽  
Norway Spruce ◽  
Genomic Prediction ◽  
Wood Quality ◽  
Tree Height ◽  
Exome Capture ◽  
Breeding Cycle ◽  
Quality Traits ◽  
Genotyping Platform

AbstractBackgroundGenomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as a genotyping platform. We used 116,765 high quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (G×E), genetic composition, size of the training and validation set, relatedness, and the number of SNPs on the accuracy and predictive ability (PA) of GS.ResultsUsing G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11–14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased more than 30% using one site as training for GS prediction to the second site, indicating that G×E for tree height should be accommodated in model fitting. Using half-sib family structure instead of full-sib led a significant reduction in GS accuracy and PA. The full-sib family structure only needed 750 makers to reach similar accuracy and PA as 100,000 markers required for half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000–8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all makers.ConclusionsThe study indicates GS would be efficient in reducing generation time of a breeding cycle in conifer tree breeding program that requires a long-term progeny testing. Sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G × E effect when a strong G×E is detected.

Genomic relationship–based genetic parameters and prospects of genomic selection for growth and wood quality traits in Eucalyptus benthamii

2021 ◽  
Vol 17 (4) ◽  
Author(s):  
João Gabriel Zanon Paludeto ◽  
Dario Grattapaglia ◽  
Regiane Abjaud Estopa ◽  
Evandro Vagner Tambarussi
Keyword(s):  
Genomic Selection ◽  
Genetic Parameters ◽  
Wood Quality ◽  
Quality Traits ◽  
Genomic Relationship ◽  
Selection For
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