International Journal of Horticulture, 2025, Vol.15, No.3, 105-112 http://hortherbpublisher.com/index.php/ijh 109 5.2 Findings and statistical interpretation The results of the meta-analysis revealed genetic differences in Brix-related traits in different materials. SSR and SNP markers performed well in identifying polymorphic sites related to Brix content (Zhou et al., 2015; Wang et al., 2017; Zhao and Qin, 2018). Multiple QTLS were consistently associated with high Brix levels and could be considered as breeding target sites. Statistical analysis showed that the genetic variation of Brix traits was influenced by both intraspecific and interspecific diversity, and the increase of Brix level in some materials was closely related to the existence of specific alleles (Duval et al., 2001; Junior et al., 2021; Nashima et al., 2022). Rattanathawornkiti et al. (2016) and Wang et al. (2017) successfully identified several genetic populations associated with superior Brix traits through principal component analysis (PCA) and cluster analysis. 5.3 Implications for breeding programs The results of the meta-analysis provided scientific basis for pineapple breeding. The identification of specific QTLS associated with high Brix values can provide theoretical basis for marker-assisted selection and help breeders develop new varieties with sweeter and better quality (Zhou et al., 2015; Junior et al., 2021; Nashima et al., 2022). The genetic diversity revealed in the study indicates that researchers can use heterosis to breed new varieties with excellent Brix characteristics (Wang et al., 2017; Ismail et al., 2020; Hayati and Kasiamdari, 2024). The incorporation of molecular markers into the breeding process will significantly improve the efficiency of parental screening and accelerate the breeding of high-quality and high-yield pineapple varieties (Duval et al., 2001; Rattanathawornkiti et al., 2016). 6 Applications in Breeding and Genomic Innovation 6.1 Marker-assisted and genomic selection Marker-assisted selection and genomic selection play a key role in modern pineapple breeding programs. The use of SSR, AFLP, SNP and other molecular markers is very helpful for assessing genetic diversity and identifying desirable traits in germplasm. Studies have shown that SSR markers are more efficient than ISSR markers in assessing genetic diversity, which is conducive to researchers' selection of parents with excellent traits (Wang et al., 2017; Ismail et al., 2020). SNP markers can provide stable and accurate DNA fingerprints, which are helpful for genotype identification and germplasm resource management, and can accelerate the screening and breeding of good genotypes (Zhou et al., 2015). 6.2 Genomic prediction models informed by meta-data The wealth of genetic information is used by genomic prediction models to predict the performance of undetermined genotypes. By integrating metadata from different studies into predictive models, researchers can significantly improve their accuracy. Nashima et al. ’s study in 2022, showed that haplotype-based genome sequencing has enabled the localization of genes associated with important traits such as leaf margin morphology and pulp color. Studies by Zhou et al. (2015) and Nashima et al. (2022) demonstrate that combining genomic information with phenotypic data can help build models that predict the performance of new hybrid combinations, speeding up the breeding cycle and improving selection efficiency. 6.3 Bridging research and practical use Translating genetic research results into practical breeding strategies will help promote the development of the pineapple industry. A number of studies have revealed that genetic identification of pineapple germplasm materials using AFLP and ISSR markers will help reveal the genetic relationship and population structure, and provide scientific basis for hybrid selection and breeding program design (Paz et al., 2012; Rattanathawornkiti et al., 2016; Wang et al., 2017). The application of genomic tools in breeding programs can help select parents with high heterosis potential and accelerate the development of quality varieties (Junior et al., 2021). 7 Limitations and Future Directions 7.1 Methodological constraints SSR and ISSR markers show moderate to high effectiveness in polymorphism detection, but the efficiency of SSR and ISSR markers is different, and some studies have shown that SSR markers are more advantageous in genetic
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