Plant Gene and Trait 2024, Vol.15, No.5, 220-229 http://genbreedpublisher.com/index.php/pgt 224 identified loci and exploring the functional roles of the candidate genes to better understand the genetic mechanisms underlying yield traits in sugarcane (Racedo et al., 2016; Barreto et al., 2019; Fickett et al., 2019). 5.3 Impact of these findings on sugarcane breeding programs The impact of the BPSG study on sugarcane breeding programs is profound. By providing a set of validated markers associated with key traits, researchers can more effectively manage crosses and select superior genotypes, thereby accelerating the development of high-yielding sugarcane varieties. The study’s approach to using a diverse panel and robust statistical models ensures that the identified markers are reliable and applicable across different breeding contexts. This can lead to more targeted and efficient breeding strategies, ultimately contributing to increased sugarcane productivity and sustainability (Barreto et al., 2019; Yang et al., 2020). 6 Integrating GWAS Findings into Breeding Programs 6.1 Strategies for incorporating GWAS results into practical breeding Incorporating GWAS findings into practical breeding programs involves several strategic steps. Firstly, the identification of significant marker-trait associations (MTAs) through GWAS provides a foundation for selecting desirable traits. For instance, studies have identified numerous MTAs for cane yield and sucrose traits in sugarcane, which can be validated and utilized in breeding programs (Racedo et al., 2016; Barreto et al., 2019). The integration process begins with the validation of these markers in diverse populations to ensure their reliability and effectiveness across different genetic backgrounds and environmental conditions (Fickett et al., 2019). Once validated, these markers can be used to develop marker-assisted selection (MAS) protocols. This involves genotyping breeding populations for the presence of favorable alleles and selecting individuals that carry these alleles for further breeding (Barreto et al., 2019; Fickett et al., 2019). Additionally, the use of genomic selection (GS) models, which incorporate genome-wide marker data to predict the breeding values of individuals, can enhance the selection process by providing more accurate predictions of genetic potential (Hayes et al., 2021; Ravelombola et al., 2021). 6.2 Marker-assisted selection (MAS) and genomic selection (GS) Marker-assisted selection (MAS) and genomic selection (GS) are two pivotal approaches for integrating GWAS findings into breeding programs. MAS focuses on the use of specific markers associated with desirable traits to guide the selection of breeding candidates. For example, significant markers identified for cane yield and sucrose traits in sugarcane can be used in MAS to select high-yielding and high-sucrose clones (Barreto et al., 2019; Fickett et al., 2019). This approach has been shown to be effective in improving traits such as disease resistance and yield components in various crops (Ravelombola et al., 2021). On the other hand, GS utilizes genome-wide marker data to predict the genetic potential of individuals. This method has been demonstrated to improve the accuracy of selection and accelerate the breeding process. In sugarcane, GS models have been developed to predict traits such as tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content, achieving high prediction accuracies (Hayes et al., 2021). The combination of MAS and GS can provide a comprehensive strategy for enhancing breeding efficiency and achieving genetic gains in sugarcane (Ravelombola et al., 2021). 6.3 Examples of successful integration in sugarcane breeding Several studies have demonstrated the successful integration of GWAS findings into sugarcane breeding programs. For instance, a study conducted on the Louisiana sugarcane core collection identified significant MTAs for cane yield and sucrose traits, which were subsequently validated and used in MAS to select superior clones. Fickett et al. (2019) formed the Louisiana sugarcane core collection using 97 clones. The heat map and dendrogram generated from the IBS K-matrix showed strong differentiation between ten clones and the rest of the collection that was divided into two main groups (Figure 2). Similarly, another study on a Brazilian panel of sugarcane genotypes identified MTAs for various yield traits, which were used to guide the selection of high-performing genotypes (Barreto et al., 2019).
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