Molecular Plant Breeding 2024, Vol.15, No.5, 247-258 http://genbreedpublisher.com/index.php/mpb 251 identified through genome sequencing and advanced genotyping techniques to predict breeding values using genetic markers alone (Goddard and Hayes, 2007). The process involves estimating the breeding value from genomic data by calculating the conditional mean of the breeding value given the genotype at each QTL, which is approximated using marker genotypes. Traditional MAS has proven effective for traits controlled by major QTLs; however, it encounters challenges when addressing quantitative traits influenced by multiple minor effect alleles. In contrast, GS estimates all marker effects simultaneously, thereby capturing the effects of both major and minor QTLs and providing a more comprehensive prediction model (Heffner et al., 2009; Merrick et al., 2022). This approach mitigates the biases associated with individual marker effect estimation and accounts for greater variation stemming from small-effect QTLs, making it more suitable for polygenic traits (Heffner et al., 2009). 4.2 Implementation in breeding programs Implementing GS in breeding programs involves several key steps. Firstly, collect high-density marker data and phenotypic data from a training population. Secondly, develop prediction models using the collected data to estimate marker effects. Then, validate the prediction models with independent test populations to ensure their accuracy. In the end, use the validated models to predict the breeding values of selection candidates and make selection decisions based on these predictions (Heffner et al., 2009; Merrick et al., 2022). GS has been successfully implemented in various plant species, significantly improving breeding efficiency. For instance, in crop improvement, GS has shown high accuracy in predicting breeding values for polygenic traits, thereby accelerating the breeding cycle and increasing genetic gains per unit time (Heffner et al., 2009). Moreover, GS has been applied to perennial ryegrass, where it has demonstrated the potential to predict breeding values with high accuracy, even for traits with low heritability (Rabier et al., 2016). 4.3 Benefits and challenges GS offers several advantages over traditional breeding methods. By incorporating all marker information, GS provides more accurate predictions of breeding values, particularly for polygenic traits (Habier et al., 2007). Additionally, GS reduces the need for extensive phenotyping in each generation, facilitating faster selection and breeding cycles. GS also captures the effects of both major and minor QTLs, providing a more holistic approach to trait improvement (Heffner et al., 2009). Despite its advantages, GS faces several challenges. Collecting high-quality genotypic and phenotypic data, essential for developing accurate prediction models, can be resource-intensive (Merrick et al., 2022). Ensuring the accuracy of these models across different populations and environments requires continuous validation and updates (Goddard and Hayes, 2007; Heffner et al., 2009). Additionally, maintaining strong linkage disequilibrium between markers and QTLs over multiple generations can be challenging, affecting the long-term accuracy of GS (Habier et al., 2007). Integrating GS into Eucommia ulmoides breeding programs holds significant potential for improving breeding efficiency and achieving greater genetic gains. However, it is crucial to address the associated challenges and continuously refine the prediction models to ensure successful implementation. 5 Integrating QTL Mapping and Genomic Selection 5.1 Synergies between QTL mapping and genomic selection Quantitative trait loci (QTL) mapping identifies specific genome regions associated with phenotypic traits, providing valuable insights into the genetic architecture of these traits. This information is crucial for GS models, which rely on high-density markers to predict breeding values. Incorporating QTL data allows for the refinement of GS models by including markers in strong linkage disequilibrium with the QTL, thus improving prediction accuracy (Goddard and Hayes, 2007; Rabier et al., 2016). For instance, integrating QTL mapping with genomic best linear unbiased prediction (GBLUP) has been shown to enhance the precision of genomic predictions by reducing false positives and increasing mapping accuracy (Li et al., 2017).
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