Plant Gene and Traits 2024, Vol.15, No.4, 207-219 http://genbreedpublisher.com/index.php/pgt 211 backgrounds and environmental interactions (Mohammadi et al., 2020; Chen et al., 2021). Additionally, the detection of rare variants and QTLs with small effects remains difficult, as current methods are more suited to identifying common variants with moderate effects (Mohammadi et al., 2020; Lima et al., 2022). Despite these challenges, there are numerous opportunities for advancing Camellia breeding through QTL mapping and GWAS. The integration of next-generation sequencing technologies and advanced statistical methods can enhance the resolution and accuracy of QTL detection. Moreover, the use of multi-parent populations, such as MAGIC (Multi-parent Advanced Generation Inter-Cross) populations, can increase the precision of QTL mapping by capturing a broader range of genetic diversity (Pascual et al., 2016). Furthermore, leveraging existing phenotypic data from breeding programs and combining it with genomic data can facilitate the identification of robust QTLs that are stable across different environments. This approach has been successfully demonstrated in long-lived tree species like Norway spruce, where extensive phenotypic data from breeding programs were standardized and used for GWAS, leading to the identification of novel QTLs associated with important traits (Chen et al., 2021). 6 Genomic Selection (GS) inCamellia Breeding 6.1 Principles of genomic selection and its benefits for perennial crops like Camellia Genomic selection (GS) is a predictive breeding approach that leverages genome-wide markers to estimate the breeding values of individuals within a population. Unlike traditional marker-assisted selection, which focuses on a few significant loci, GS incorporates all available marker data to predict the performance of genotypes more accurately (Crossa et al., 2017). This comprehensive approach is particularly beneficial for complex traits controlled by many genes with small effects, which are common in perennial crops like Camellia (Wang et al., 2018). The primary advantage of GS in perennial crops is the acceleration of the breeding cycle. By predicting the genetic potential of seedlings before they reach maturity, breeders can make selection decisions earlier, thus reducing the time required to develop new varieties (Figure 2) (Cappetta et al., 2020). Additionally, GS can enhance genetic gain per unit time and cost by improving selection accuracy and intensity (Xu et al., 2019). This is crucial for perennial crops, which typically have long generation intervals and require significant resources for phenotypic evaluation. Figure 2 Comparison of genomic selection (GS) and conventional selection in tomato breeding programs (Adopted from Cappetta et al., 2020) Image caption: Screening of recombinant lines through GS approaches optimizes the genetic gain obtained in each selection cycle. Breeding cycles (horizontal dashed lines) are shortened by removing phenotypic evaluation of lines before training population (TRN) evaluation for the next cycle (Adopted from Cappetta et al., 2020)
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