Plant Gene and Trait 2024, Vol.15, No.5, 220-229 http://genbreedpublisher.com/index.php/pgt 226 novel genes associated with agronomic traits that were previously undetectable using standard SNP analysis (Yano et al., 2016). Additionally, the development of more sophisticated statistical models and computational tools has enhanced the accuracy and speed of GWAS, allowing for the detection of rare variants and synthetic associations (Liu et al., 2018; Cortes et al., 2021). These technological advancements are expected to further refine the resolution of GWAS, thereby facilitating the discovery of key genetic loci that can be targeted for crop improvement. Figure 3 Prediction accuracy (A) and coincidence index (B) of the genomic estimated breeding estimated value (GEBV) of seven yield- and sugar-related traits for fivefold cross-validation (fivefold CV) of seven genomic selection (GS) methods (Adopted from Islam et al., 2022) Image caption: The fivefold CV was performed by randomly selecting four-fifths of the individuals for training and reaming a fifth as the validation population for the plant cane. The seven traits were Brix, fiber content (FC), pol, sucrose content (SC), stalk diameter (SD), stalk population (SP), and stalk weight (SW) (Adopted from Islam et al., 2022) 7.2 Challenges in translating GWAS findings to field applications Despite the progress in GWAS, several challenges remain in translating these findings into practical applications in the field. One major hurdle is the complex polyploid genome of sugarcane, which complicates the identification of marker-trait associations (MTAs) and the subsequent validation of these markers in diverse populations (Barreto et al., 2019). The large extent of linkage disequilibrium (LD) in sugarcane also poses a challenge, as it can lead to the identification of false positives and obscure true associations (Yano et al., 2016). Moreover, the genetic diversity and population structure within sugarcane breeding populations can affect the reproducibility of GWAS results, making it difficult to apply these findings universally (Racedo et al., 2016). Another significant challenge is the integration of GWAS data with phenotypic data from field trials, which is essential for the effective implementation of marker-assisted selection (MAS) and genetic engineering (Fickett et al., 2019). Addressing these challenges requires a concerted effort to develop more robust and scalable GWAS methodologies that can account for the unique genetic architecture of sugarcane. 7.3 Future research directions to overcome current limitations To overcome the current limitations in GWAS for sugarcane, future research should focus on several key areas. First, there is a need for larger and more diverse breeding populations to increase the statistical power of GWAS
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