462
number of genes (Bernardo 2008; Grattapaglia et al. 2018). High-throughput
sequencing technologies (HTS) allowed a shift in the strategies of analyses to overcome the limitation of MAS (Minamikawa et al. 2018). One approach is the
genome-wide association study (GWAS) which provides detection of quantitative
trait loci (QTLs) for the target traits. However, if the objective is to select superior
individuals for the breeding programmes based on their genomic estimated breeding values, the genomic selection (GS) is the best approach at present (Meuwissen
et al. 2001; Cappa et al. 2019).
In wild forest tree species, where the gene pools have not been broadly changed
for breeding purposes in comparison to crops and cultivated forest trees, the combination of these strategies (GWAS, GS and MAS) will not only accelerate the breeding cycles, but also improve the design of the breeding programmes (Grattapaglia
et al. 2018; Minamikawa et al. 2018). For example, given that forest breeding programmes are conditional on the length of the breeding cycle, the application of
genomic tools could maximize the genetic gain per unit of time by the early selection of juvenile individuals based on their genomic estimated breeding values. In
addition, the genetic information obtained by the utilization of the HTS could help
to study not only the genetic variation in the native populations but also to integrate
the environmental data for evaluating the performance of the genetic material in
different scenarios, which could improve the design of the breeding programmes.
17.3.1 Genome-Wide Association Studies
Finding the connection between the genotype information and complex phenotype
has been one of the fundamental goals of plant genetics research, mainly to understand how this knowledge can be applied directly to the breeding programmes
(Botstein and Risch 2003). This is so because in most of the complex traits of agricultural importance, the phenotypic variation and their interaction with the environment are controlled by multiple QTLs. Accordingly, in the last decade, there have
been a lot of scientific publications where the search for molecular markers associated with QTLs was carried out to identify genes responsible for the quantitative
variation of different complex traits (Thornsberry et al. 2001; Blott et al. 2003;
Aranzana et al. 2005; Zhu et al. 2008).
Genome-wide association studies (GWAS) is a powerful tool to identify statistical associations between a genetic variant and a specific phenotype through the
utilization of a large number of molecular markers distributed along the genome
(Brachi et al. 2011; Beilsmith et al. 2019). This genomic approach consists in the
use of individuals collected from natural populations or groups of individuals often
of unknown ancestry, to detect the genetic variability by exploiting the historical
recombination events and the natural genetic diversity (Risch and Merikangas 1996;
Nordborg and Tavare 2002; Zhu et al. 2008). In other words, it relies on the linkage
disequilibrium (LD) where nearby genetic polymorphisms are associated more frequently than by chance. This approach offers an interesting strategy for forest
S. L. Torales et al.
number of genes (Bernardo 2008; Grattapaglia et al. 2018). High-throughput
sequencing technologies (HTS) allowed a shift in the strategies of analyses to overcome the limitation of MAS (Minamikawa et al. 2018). One approach is the
genome-wide association study (GWAS) which provides detection of quantitative
trait loci (QTLs) for the target traits. However, if the objective is to select superior
individuals for the breeding programmes based on their genomic estimated breeding values, the genomic selection (GS) is the best approach at present (Meuwissen
et al. 2001; Cappa et al. 2019).
In wild forest tree species, where the gene pools have not been broadly changed
for breeding purposes in comparison to crops and cultivated forest trees, the combination of these strategies (GWAS, GS and MAS) will not only accelerate the breeding cycles, but also improve the design of the breeding programmes (Grattapaglia
et al. 2018; Minamikawa et al. 2018). For example, given that forest breeding programmes are conditional on the length of the breeding cycle, the application of
genomic tools could maximize the genetic gain per unit of time by the early selection of juvenile individuals based on their genomic estimated breeding values. In
addition, the genetic information obtained by the utilization of the HTS could help
to study not only the genetic variation in the native populations but also to integrate
the environmental data for evaluating the performance of the genetic material in
different scenarios, which could improve the design of the breeding programmes.
17.3.1 Genome-Wide Association Studies
Finding the connection between the genotype information and complex phenotype
has been one of the fundamental goals of plant genetics research, mainly to understand how this knowledge can be applied directly to the breeding programmes
(Botstein and Risch 2003). This is so because in most of the complex traits of agricultural importance, the phenotypic variation and their interaction with the environment are controlled by multiple QTLs. Accordingly, in the last decade, there have
been a lot of scientific publications where the search for molecular markers associated with QTLs was carried out to identify genes responsible for the quantitative
variation of different complex traits (Thornsberry et al. 2001; Blott et al. 2003;
Aranzana et al. 2005; Zhu et al. 2008).
Genome-wide association studies (GWAS) is a powerful tool to identify statistical associations between a genetic variant and a specific phenotype through the
utilization of a large number of molecular markers distributed along the genome
(Brachi et al. 2011; Beilsmith et al. 2019). This genomic approach consists in the
use of individuals collected from natural populations or groups of individuals often
of unknown ancestry, to detect the genetic variability by exploiting the historical
recombination events and the natural genetic diversity (Risch and Merikangas 1996;
Nordborg and Tavare 2002; Zhu et al. 2008). In other words, it relies on the linkage
disequilibrium (LD) where nearby genetic polymorphisms are associated more frequently than by chance. This approach offers an interesting strategy for forest
S. L. Torales et al.