463
genetics since it allows studying simultaneously various regions of the genome
without the construction of a specific mapping population and shows a high-resolution power, in particular, when the population under study has low LD (Alvarez
et al. 2014). For example, 17 SNPs were potentially detected associated with serotiny in a local population of Pinus pinaster (Budde et al. 2014). This study showed
not only the application of GWAS but also its promising utilization in non-model
species in their natural environments. On the other hand, it was stressed the usefulness of combining common garden experiments of non-model species with genomewide association studies in several populations and especially for detecting genome
markers for locally adapted traits that differentiate among populations (De
Villemereuil et al. 2016).
GWAS analysis was reviewed in Naidoo et al. (2019) to explore tolerance to
biotic stress in forest trees and in Du et al. (2018) to analyse wood properties traits.
Some reports were carried out for identifying the loci that contribute to adaptive
variation in several species like Populus trichocarpa (Chhetri et al. 2019),
Handroanthus impetiginosus (Collevatti et al. 2019) and Pinus contorta (Mahony
et al. 2019).
17.3.2 Genomic Selection
The perspective of accelerating and improving the accuracy of selection by using
molecular markers in forest tree species has led to the application of different methodologies in order to locate the loci affecting the traits of interest (Sewell and Neale
2000; Neale 2007). By contrast, genomic selection (GS) is based on the simultaneous estimation of the effects of all available markers along the genome to predict
individually breeding values, without specific knowledge of the genes that contribute to a particular character or location (Meuwissen et al. 2001).
The approach of GS uses a “training population”, with genotypic and phenotypic
data available, to fit a predictive model. This model is then used to predict the
genomic breeding values of seedling progeny in future generations from its genotypic data. The prediction is used to generate a ranking to select superior individuals
to be planted in the field. Compared with other methodologies, GS shows higher
accuracy and allows shortening the improvement cycles by 50% (Grattapaglia and
Resende 2011), which implies a gain associated with early elite clone selection.
Also, it reduces the costs in the maintenance of the progeny tests and minimizes the
measurements with the consequent reduction in logistics and personnel cost.
Besides, GS is useful to improve multiple traits at one time (particularly for low
heritability traits) by generating independent predictive models that allow the generation of selection indexes. This allows the breeder to select at an early age and
optimize controlled crosses, by choosing mating pairs that provide their progenies
with the best allelic composition for the traits to be improved (Toro and Varona 2010).
A “training population” for the breeding programme of a wild native species
through GS implies the establishment of a seed orchard, as far as possible with
17 Application of High-Throughput Sequencing Technologies in Native Forest Tree…
genetics since it allows studying simultaneously various regions of the genome
without the construction of a specific mapping population and shows a high-resolution power, in particular, when the population under study has low LD (Alvarez
et al. 2014). For example, 17 SNPs were potentially detected associated with serotiny in a local population of Pinus pinaster (Budde et al. 2014). This study showed
not only the application of GWAS but also its promising utilization in non-model
species in their natural environments. On the other hand, it was stressed the usefulness of combining common garden experiments of non-model species with genomewide association studies in several populations and especially for detecting genome
markers for locally adapted traits that differentiate among populations (De
Villemereuil et al. 2016).
GWAS analysis was reviewed in Naidoo et al. (2019) to explore tolerance to
biotic stress in forest trees and in Du et al. (2018) to analyse wood properties traits.
Some reports were carried out for identifying the loci that contribute to adaptive
variation in several species like Populus trichocarpa (Chhetri et al. 2019),
Handroanthus impetiginosus (Collevatti et al. 2019) and Pinus contorta (Mahony
et al. 2019).
17.3.2 Genomic Selection
The perspective of accelerating and improving the accuracy of selection by using
molecular markers in forest tree species has led to the application of different methodologies in order to locate the loci affecting the traits of interest (Sewell and Neale
2000; Neale 2007). By contrast, genomic selection (GS) is based on the simultaneous estimation of the effects of all available markers along the genome to predict
individually breeding values, without specific knowledge of the genes that contribute to a particular character or location (Meuwissen et al. 2001).
The approach of GS uses a “training population”, with genotypic and phenotypic
data available, to fit a predictive model. This model is then used to predict the
genomic breeding values of seedling progeny in future generations from its genotypic data. The prediction is used to generate a ranking to select superior individuals
to be planted in the field. Compared with other methodologies, GS shows higher
accuracy and allows shortening the improvement cycles by 50% (Grattapaglia and
Resende 2011), which implies a gain associated with early elite clone selection.
Also, it reduces the costs in the maintenance of the progeny tests and minimizes the
measurements with the consequent reduction in logistics and personnel cost.
Besides, GS is useful to improve multiple traits at one time (particularly for low
heritability traits) by generating independent predictive models that allow the generation of selection indexes. This allows the breeder to select at an early age and
optimize controlled crosses, by choosing mating pairs that provide their progenies
with the best allelic composition for the traits to be improved (Toro and Varona 2010).
A “training population” for the breeding programme of a wild native species
through GS implies the establishment of a seed orchard, as far as possible with
17 Application of High-Throughput Sequencing Technologies in Native Forest Tree…