Molecular Plant Breeding 2024, Vol.15, No.5, 247-258 http://genbreedpublisher.com/index.php/mpb 249 with favorable genotypes. The genetic linkage map developed using SSR and SNP markers has been employed to identify QTLs associated with growth traits, aiding in the selection of superior genotypes (Li et al., 2014; Jin et al., 2020). The identification of QTLs for growth traits in Eucommia ulmoides has been a major focus of genetic mapping studies. Several QTLs associated with growth-related traits such as tree height, ground diameter, and crown diameter have been identified. One study detected 44 QTLs for growth traits on various linkage groups, with phenotypic variance ranging from 10.0% to 14.2% (Liu et al., 2022). Another study identified 89 hypothetical QTLs for growth traits measured over ten years, highlighting the genetic basis of these traits and providing targets for MAS (Jin et al., 2020). 3 QTL Mapping inEucommia ulmoides 3.1 Methodologies for QTL mapping Traditional QTL mapping techniques in E. ulmoides have primarily relied on the construction of genetic linkage maps using various molecular markers. For instance, a genetic linkage map was constructed using SRAP, AFLP, ISSR, and SSR markers. The previous map constructed by Li et al. (2014) using aforementioned molecular markers covered approximately 89% of the E. ulmoides genome, with an average distance of 3.1 cM between adjacent markers, facilitating the identification of 18 QTLs associated with growth traits. Additionally, another study refined the genetic linkage map using 452 polymorphic markers from 365 SSR primers, covering 94.10% of the estimated genome and identifying 89 hypothetical QTLs for growth traits (Jin et al., 2020). With the advancement of sequencing technologies and decreasing costs, bulked segregant analysis (BSA) and QTL-seq have emerged as powerful techniques for rapid QTL mapping. BSA involves pooling DNA from individuals exhibiting extreme phenotypes to identifying significant SNPs associated with the trait of interest. For example, PyBSASeq, a Python-based algorithm, has been developed to enhance the sensitivity of BSA-Seq, enabling the detection of SNP-trait associations at lower sequencing coverage and reduced costs (Zhang and Panthee, 2020). QTL-seq, another analytical method, combines whole-genome resequencing of bulked populations to identify QTLs, which has been successfully applied to various plant species, including rice, for the rapidly mapping of QTLs associated with traits such as disease resistance and seedling vigor (Takagi et al., 2013). 3.2 Identified QTLs for key traits Several studies have identified QTLs associated with growth traits in E. ulmoides. A high-density genetic map constructed using GBS identified 44 QTLs linked to growth traits on linkage groups LG02, LG06, LG07, LG08, and LG10, accounting for 10.0% to 14.2% of the phenotypic variance (Liu et al., 2022). Additionally, another study revealed 25 QTLs for tree height, 32 QTLs for ground diameter, and 15 QTLs for crown diameter, highlighting the significant correlation of growth traits measured over a span of ten years (Jin et al., 2020). While the primary focus has been on growth traits, there is also interest in identifying QTLs for secondary metabolites and yield-related traits. Although specific QTLs associated with these traits in E. ulmoides have not been extensively reported, methodologies such as QTL-seq and BSA-Seq, which have been successfully applied in other species, hold promise for future research. For instance, QTG-seq has been utilized to fine-map QTLs in maize to identify candidate genes for plant height (Figure 1) (Zhang et al., 2019). Similar approaches could be adapted for E. ulmoides. 3.3 Challenges and limitations Traditional QTL mapping techniques encounter several limitations, such as the requirement for large populations and extensive genotyping efforts. The resolution of QTL mapping is frequently constrained by markers density and the size of the mapping population, which can lead to broad confidence intervals for QTL locations (Li et al., 2014; Jin et al., 2020). Furthermore, the time-consuming and labor-intensive nature of developing and selecting DNA markers for linkage analysis presents significant challenges (Takagi et al., 2013).
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