Molecular Plant Breeding 2025, Vol.16, No.1, 1-12 http://genbreedpublisher.com/index.php/mpb 8 5.3 Challenges in reproducibility and validation of QTLs Reproducibility and validation of QTLs remain challenging due to several factors. One major issue is the genetic background of the populations used in different studies. For instance, Lee et al. (2020) utilized a diverse germplasm collection to conduct a genome-wide association study, which may lead to the identification of QTLs that are not easily reproducible in other populations. Additionally, the presence of minor-effect QTLs, as discussed in Wang et al. (2020a), complicates the validation process since these QTLs may not consistently express in different genetic backgrounds or environmental conditions. Another challenge is the fine mapping and cloning of QTLs, which is often required for validation. As noted by Pan et al. (2022), fine mapping of the major-effect QTL FS5.2 involved developing near-isogenic lines and detailed genetic analysis, which is resource-intensive and time-consuming. Despite these challenges, efforts to standardize QTL nomenclature and collaborative research, as recommended by Wang et al. (2020b), can improve the reproducibility and validation of QTLs in cucumber research. 6 Integration of QTL Mapping with Genomic Selection 6.1 Opportunities for combining QTL mapping with genomic selection for trait prediction The integration of Quantitative Trait Loci (QTL) mapping with genomic selection (GS) presents a promising approach for enhancing trait prediction in cucumber breeding programs. QTL mapping identifies specific genomic regions associated with phenotypic traits, while GS uses genome-wide markers to predict the genetic value of individuals. Combining these methods can leverage the strengths of both, improving the accuracy and efficiency of breeding programs. QTL mapping has been extensively used to identify loci associated with important agronomic traits in cucumber, such as fruit size, shape, and disease resistance (Gao et al., 2020; Pan et al., 2020; Wang et al., 2020b). These identified QTLs can serve as valuable markers in GS models, enhancing the prediction accuracy for complex traits. For instance, the integration of QTL information into GS models can help in the selection of individuals with desirable traits, even in the absence of phenotypic data (Liu et al., 2021). This is particularly useful for traits with low heritability or those that are difficult to measure. Moreover, the use of high-density genetic maps and advanced genotyping techniques, such as genotyping-by-sequencing (GBS), has facilitated the identification of numerous QTLs across the cucumber genome (Yang et al., 2019; Lee et al., 2020). These advancements provide a rich source of genetic markers that can be incorporated into GS models, thereby improving the prediction of genetic merit and accelerating the breeding cycle. 6.2 Case studies and examples in cucumber breeding programs Several cucumber breeding programs have successfully integrated QTL mapping with genomic selection to enhance trait prediction and selection efficiency. For example, a study on the cucumber inbred line WI2757 identified multiple QTLs for traits such as flowering time, fruit length, and disease resistance (Pan et al., 2020). By incorporating these QTLs into GS models, breeders can more accurately predict the performance of new hybrids, thereby improving the selection process. Another notable example is the use of genomic prediction in the breeding of cucumber plants, where 81 inbred lines were genotyped, and 16 662 markers were identified to represent the genetic background of cucumber (Liu et al., 2021). The study demonstrated that the predictive ability for 12 commercial traits ranged from 0.38 to 0.95 under cross-validation strategies, highlighting the potential of integrating QTL mapping with GS for trait prediction. Additionally, the development of near-isogenic lines (NILs) for major-effect QTLs, such as FS5.2 for fruit size and shape, has provided valuable insights into the genetic control of these traits (Figure 3) (Pan et al., 2022). By fine-mapping these QTLs and incorporating the associated markers into GS models, breeders can achieve more precise selection for desired fruit characteristics.
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