GAB_2024v15n3

Genomics and Applied Biology 2024, Vol.15, No.3, 142-152 http://bioscipublisher.com/index.php/gab 147 map and identified 89 QTLs related to growth traits over a decade, highlighting the potential for long-term genetic improvement (Jin et al., 2020). These approaches are essential for dissecting the genetic architecture of complex traits and enhancing breeding efficiency in E. ulmoides. Figure 2 Collinearity analyses of the MYB gene family betweenE. ulmoides and four other species (Adopted from Hu et al., 2023) Image caption: From top to bottom, the species collinearity analysis of E. ulmoides-Vitis vinifera (yellow), E. ulmoides-Arabidopsis thaliana (green), E. ulmoides-Coffea canephora (brown), E. ulmoides-Sorghum bicolor (blue). Gray lines in the background indicate the collinear blocks within E. ulmoides and different plant genomes, whereas red lines highlight syntenic MYB gene pairs (Adopted from Hu et al., 2023) 6 Challenges and Limitations in Integrating Functional Genomics and Breeding 6.1 Technical challenges: data analysis, genomic data integration, and interpretation Integrating functional genomics with breeding in Eucommia ulmoides presents several technical challenges. One significant issue is the complexity of data analysis and the integration of vast genomic datasets. For instance, the high-quality chromosome-level genome assembly of E. ulmoides, which includes 31 665 protein-coding genes, requires sophisticated bioinformatics tools for accurate data interpretation and integration (Du et al., 2023). Additionally, the construction of high-density genetic maps using genotyping-by-sequencing (GBS) and single-nucleotide polymorphism (SNP) markers involves managing and analyzing large volumes of data, which can be technically demanding (Liu et al., 2022). The identification and mapping of quantitative trait loci (QTL) for growth traits further complicate the data analysis process, as it requires long-term phenotypic data collection and advanced statistical methods to correlate genetic markers with phenotypic traits (Li et al., 2014; Jin et al., 2020). 6.2 Economic and logistical challenges in deploying advanced genomics in breeding programs Deploying advanced genomic technologies in breeding programs for E. ulmoides also faces economic and logistical challenges. The cost of high-throughput sequencing technologies, such as PacBio and Hi-C, and the subsequent data analysis can be prohibitive for many research institutions and breeding programs (Li et al., 2020; Du et al., 2023). Additionally, the establishment and optimization of techniques like AFLP (Amplified Fragment Length Polymorphism) require significant investment in laboratory infrastructure and technical expertise (Dawei et al., 2010; Wang et al., 2011). Logistically, the long juvenile phase of E. ulmoides, which delays the identification of sex and other important traits, poses a challenge for timely breeding interventions (Wang et al., 2011). The need for extensive field trials to validate genomic predictions further adds to the logistical burden, requiring substantial time and resources (Jin et al., 2020; Liu et al., 2022).

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