scholarly journals Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1190
Author(s):  
Vadim G. Lebedev ◽  
Tatyana N. Lebedeva ◽  
Aleksey I. Chernodubov ◽  
Konstantin A. Shestibratov
Keyword(s):  
Molecular Markers ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Models ◽  
Forest Tree ◽  
Tree Improvement ◽  
Breeding Value ◽  
Breeding Cycle ◽  
Forest Trees ◽  
Forest Tree Breeding

The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.

scholarly journals Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

2021 ◽  
Vol 22 (19) ◽  
pp. 10583
Author(s):  
Sunny Ahmar ◽  
Paulina Ballesta ◽  
Mohsin Ali ◽  
Freddy Mora-Poblete
Keyword(s):  
Genome Editing ◽  
Prediction Models ◽  
Association Studies ◽  
Tree Breeding ◽  
Forest Tree ◽  
Transformation Method ◽  
Breeding Cycle ◽  
Genome Wide Association Studies ◽  
Forest Trees ◽  
Forest Tree Breeding

Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.

scholarly journals Advances and Perspectives of Transgenic Technology and Biotechnological Application in Forest Trees

2021 ◽  
Vol 12 ◽  
Author(s):  
Yiyi Yin ◽  
Chun Wang ◽  
Dandan Xiao ◽  
Yanting Liang ◽  
Yanwei Wang
Keyword(s):  
Genetic Transformation ◽  
Molecular Genetic ◽  
Forest Tree ◽  
Breeding Cycle ◽  
Forest Trees ◽  
Transgenic Technology ◽  
Forest Tree Breeding ◽  
Exogenous Dna ◽  
Advantages And Disadvantages ◽  
Gm Trees

Transgenic technology is increasingly used in forest-tree breeding to overcome the disadvantages of traditional breeding methods, such as a long breeding cycle, complex cultivation environment, and complicated procedures. By introducing exogenous DNA, genes tightly related or contributed to ideal traits—including insect, disease, and herbicide resistance—were transferred into diverse forest trees, and genetically modified (GM) trees including poplars were cultivated. It is beneficial to develop new varieties of GM trees of high quality and promote the genetic improvement of forests. However, the low transformation efficiency has hampered the cultivation of GM trees and the identification of the molecular genetic mechanism in forest trees compared to annual herbaceous plants such as Oryza sativa. In this study, we reviewed advances in transgenic technology of forest trees, including the principles, advantages and disadvantages of diverse genetic transformation methods, and their application for trait improvement. The review provides insight into the establishment and improvement of genetic transformation systems for forest tree species. Challenges and perspectives pertaining to the genetic transformation of forest trees are also discussed.

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3372
Author(s):  
Cesar A. Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Medicago Sativa ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Breeding Value ◽  
Phenotypic Traits ◽  
Genome Wide ◽  
Medicago Sativa L

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

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

Genomic selection in forest tree breeding

2010 ◽  
Vol 7 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Dario Grattapaglia ◽  
Marcos D. V. Resende
Keyword(s):  
Genomic Selection ◽  
Tree Breeding ◽  
Forest Tree ◽  
Forest Tree Breeding

Incompatibility in forest trees

1975 ◽  
Vol 188 (1092) ◽  
pp. 313-326 ◽  
Keyword(s):  
Pollen Tube ◽  
Pollen Tube Growth ◽  
Genetic Load ◽  
Forest Tree ◽  
Breeding Systems ◽  
Tube Growth ◽  
Alternative Mechanism ◽  
Forest Trees ◽  
Forest Tree Breeding ◽  
Interspecific Incompatibility

Despite the great importance for forest tree breeding, very limited knowledge is yet available about the breeding systems of forest trees. Where incompatibility has been studied in the hardwoods; patterns have been observed which confirm the general rules detected for other angiosperms. Self- and interspecific incompatibility at the level of pollen tube growth has been reported for example in Betula and Alnus . In Alnus one case of unilateral interspecific incompatibility has been found. Self-incompatibility has, so far, not been reported from the conifers. Inter-specific incompatibility in the form of the arrestment of the pollen tube growth in the nucellus tissue has been observed in Picea and is particularly clear in Pinus crosses between the subgenera and Haploxylon and Diploxylon , but also within the Diploxylon -group. The nature of the incompatibility mechanism is still unknown, but serological differences related to the behaviour in the crosses has been detected in birch and pine pollen. It is suggested that the complex polysaccharidic composition of the cell walls and membranes might form a specific stereochemical basis for the incompatibility reaction. The presence of a combination of self-pollination, polyembryony and genetic load is discussed as an alternative mechanism favouring outbreeding in the Gymnosperms.

scholarly journals Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Plants ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1236
Author(s):  
Elisa Cappetta ◽  
Giuseppe Andolfo ◽  
Antonio Di Matteo ◽  
Amalia Barone ◽  
Luigi Frusciante ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Complex Traits ◽  
Breeding Value ◽  
Critical Parameters ◽  
Breeding Programs ◽  
Selection Strategies ◽  
Genome Wide ◽  
Line Selection ◽  
Tomato Breeding ◽  
Accuracy Of Prediction

Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

2021 ◽  
Author(s):  
Cesar A Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Agronomic Traits ◽  
Association Studies ◽  
Breeding Value ◽  
Genome Wide Association Studies ◽  
Genome Wide

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa, previous results indicated that low to moderate prediction accuracy values (&lt;70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. This is the first report in alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

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