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 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 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 BREEDING FOR DISEASE RESISTANCE IN FOREST TREES

1962 ◽  
Vol 38 (3) ◽  
pp. 356-362 ◽  
Author(s):  
C. Heimburger
Keyword(s):  
Disease Resistance ◽  
Exotic Species ◽  
Tree Breeding ◽  
Forest Tree ◽  
Forest Trees ◽  
Breeding Methods ◽  
Forest Tree Breeding ◽  
White Pine ◽  
Eastern White Pine ◽  
Blister Rust

Breeding for disease resistance in forest trees is a specialized kind of forest tree breeding. With breeding of white pines for resistance to blister rust as an example, the various problems encountered and solved are described. Resistance to blister rust in eastern white pine has thus far been found to be inherited on a polygenic basis. This influences the choice of effective breeding methods and the silvicultural use of the resistant materials obtained. The genetic basis of superior resistance found in exotic species, such as Balkan white pine, Japanese white pine and Himalayan white pine is also influencing the breeding methods. Because of its early flowering, breeding work with Balkan white pine has progressed further than with other exotic species. Indications have been obtained that resistance in this species is also based on polygenes. Some of these are complementary to those found in eastern white pine. In Himalayan white pine materials the presence of recessive major genes for resistance as well as polygenes is probable. The possible use of these findings in the development of resistant white pine materials and their use in the establishment of artificially and naturally regenerated stands is discussed.

scholarly journals Functional Quantitative Trait Loci (QTL) analysis for adaptive traits in a three-generation Scots pine pedigree

2018 ◽  
Author(s):  
A Calleja-Rodriguez ◽  
Z Li ◽  
H R Hallingbäck ◽  
M J Sillanpää ◽  
X Wu H ◽  
...  
Keyword(s):  
Tree Breeding ◽  
Forest Tree ◽  
Breeding Cycle ◽  
Forest Tree Breeding ◽  
Ecological Value ◽  
Adaptive Traits ◽  
Phenotypic Data ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Strong Candidate

AbstractIn forest tree breeding, QTL identification aims to accelerate the breeding cycle and increase the genetic gain of traits with economical and ecological value. In our study, both phenotypic data and predicted breeding values were used in the identification QTL linked to the adaptive value in a three-generation pedigree population, for the first time in a conifer species (Pinus sylvestris L.). A total of 11 470 open pollinated F2-progeny trees established at three different locations, were measured for growth and adaptive traits. Breeding values were predicted for their 360 mothers, originating from a single cross of two parents. A multilevel LASSO association analysis was conducted to detect QTL using genotypes of the mothers with the corresponding phenotypes and estimated breeding values (EBVs). Different levels of genotype-by-environment (G×E) effects among sites and ages were detected for survival and height. Moderate-to-low narrow sense heritabilities and EBVs accuracies were found for all traits and all sites. We identified 18 AFLPs and 12 SNPs to be associated with QTL for one or more traits. 62 QTL were significant with percentages of variance explained ranging from 1.7 to 18.9%, mostly for traits based on phenotypic data. Two SNP-QTL showed pleiotropic effects for traits related with survival, seed and flower production. Furthermore, we detected several QTL with significant effects across multiple ages, which could be considered as strong candidate loci for early selection. The lack of reproducibility of some QTL detected across sites may be due to environmental heterogeneity and QTL-by-environment effects.

scholarly journals Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shuquan Rao ◽  
Yao Yao ◽  
Daniel E. Bauer
Keyword(s):  
Genome Editing ◽  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Functional Genetics ◽  
Genome Wide ◽  
Causal Variants ◽  
Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

scholarly journals 297 GWAS for complex models accounting for populations structure with GBLUP and ssGBLUP

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 32-32
Author(s):  
Juan P Steibel ◽  
Ignacio Aguilar
Keyword(s):  
Hypothesis Testing ◽  
Large Scale ◽  
Mixed Model ◽  
Prediction Models ◽  
Association Studies ◽  
Least Square ◽  
Type I ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Formal Hypothesis Testing

Abstract Genomic Best Linear Unbiased Prediction (GBLUP) is the method of choice for incorporating genomic information into the genetic evaluation of livestock species. Furthermore, single step GBLUP (ssGBLUP) is adopted by many breeders’ associations and private entities managing large scale breeding programs. While prediction of breeding values remains the primary use of genomic markers in animal breeding, a secondary interest focuses on performing genome-wide association studies (GWAS). The goal of GWAS is to uncover genomic regions that harbor variants that explain a large proportion of the phenotypic variance, and thus become candidates for discovering and studying causative variants. Several methods have been proposed and successfully applied for embedding GWAS into genomic prediction models. Most methods commonly avoid formal hypothesis testing and resort to estimation of SNP effects, relying on visual inspection of graphical outputs to determine candidate regions. However, with the advent of high throughput phenomics and transcriptomics, a more formal testing approach with automatic discovery thresholds is more appealing. In this work we present the methodological details of a method for performing formal hypothesis testing for GWAS in GBLUP models. First, we present the method and its equivalencies and differences with other GWAS methods. Moreover, we demonstrate through simulation analyses that the proposed method controls type I error rate at the nominal level. Second, we demonstrate two possible computational implementations based on mixed model equations for ssGBLUP and based on the generalized least square equations (GLS). We show that ssGBLUP can deal with datasets with extremely large number of animals and markers and with multiple traits. GLS implementations are well suited for dealing with smaller number of animals with tens of thousands of phenotypes. Third, we show several useful extensions, such as: testing multiple markers at once, testing pleiotropic effects and testing association of social genetic effects.

scholarly journals Polygenic influences associated with adolescent cognitive skills

2021 ◽  
Author(s):  
Brittany L Mitchell ◽  
Narelle K Hansell ◽  
Kerrie McAloney ◽  
Nicholas G. Martin ◽  
Margaret J. Wright ◽  
...  
Keyword(s):  
Mental Health ◽  
Genetic Predisposition ◽  
Prediction Models ◽  
Association Studies ◽  
Developmental Trajectory ◽  
Cognitive Outcomes ◽  
Cognitive Domain ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Negatively Associated

Genes play an important role in children’s cognitive ability through puberty and into adolescence. Recent advances in genomics has enabled us to test the effect of various genetic predispositions on measured cognitive outcomes. Here, we leveraged summary statistics from the most recent genome-wide association studies of eleven cognitive and mental health traits to build polygenic prediction models of measured intelligence and academic achievement in a cohort of Australian adolescent twins (N=2,335, 57% female). Additionally, we tested the association of these polygenic risk scores (PRS) with core academic skills such as the ability to comprehend, structure and sequence, evaluate and assess, communicate, and apply techniques and procedures. We show that PRSs for educational attainment, intelligence and cognitive factors explained up to 10% of the variance in educational achievement and 7% in intelligence test scores in our cohort. Additionally, we found that a genetic predisposition for ADHD was negatively associated with all cognitive outcomes and skills and a genetic predisposition for schizophrenia was negatively associated with performance IQ but no other cognitive domain. In this study, we show the potential value of genotypic data for predicting pupil achievement and cognitive developmental trajectory through puberty and into adolescence. We provide evidence that a genetic vulnerability to some mental health disorders is associated with poorer cognitive and educational outcomes, regardless of whether the individual has developed the disorder.

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