Plant Gene and Trait 2024, Vol.15, No.5, 220-229 http://genbreedpublisher.com/index.php/pgt 225 Figure 2 Heat map and dendrogram generated with the kinship matrix (K) (Adopted from Fickett et al., 2019) Image caption: The K-matrix is an identity by state matrix of the 97 clones using 6 534 SNP and InDel markers in sugarcane (Adopted from Fickett et al., 2019) Fickett et al. (2019) highlights the genetic diversity within the studied sugarcane population. High similarity clusters suggest close genetic relationships, useful for identifying potential parent pairs in breeding programs to enhance desired traits. Conversely, areas of low similarity indicate genetic diversity, which is crucial for maintaining a robust breeding pool. The kinship matrix aids in selecting genetically diverse parents to avoid inbreeding and maximize heterosis in offspring, contributing to improved sugarcane breeding strategies. Moreover, the application of GS in sugarcane breeding has also shown promising results. Islam et al. (2022) studied the sugar and yield-related trait data from 432 sugarcane clones and 10 435 single nucleotide polymorphisms (SNPs) by using seven different GS models (Figure 3). These examples highlight the potential of integrating GWAS findings into practical breeding strategies to improve yield and agronomic traits in sugarcane. Islam et al. (2022) highlights the potential of the GS methods to predict breeding values and select superior sugarcane genotypes effectively. Panel A shows that prediction accuracy varies among the traits and methods, with values generally ranging between 0.1 and 0.3. Stalk weight (SW) demonstrates slightly higher prediction accuracy across methods, while other traits show similar moderate accuracy. Panel B presents the coincidence index, which measures the proportion of top individuals correctly identified by the models. The index is relatively consistent across traits and methods, typically around 0.2 to 0.3, indicating moderate model performance in identifying top performers. 7 Future Prospects and Challenges 7.1 Emerging technologies and their potential impact on GWAS The field of genome-wide association studies (GWAS) in sugarcane is poised for significant advancements with the integration of emerging technologies. The advent of next-generation sequencing (NGS) and high-throughput genotyping platforms has already revolutionized GWAS by enabling the rapid and cost-effective identification of genetic variants across the genome. Whole-genome sequencing, in particular, has shown promise in identifying
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