Tree Genetics and Molecular Breeding 2024, Vol.14, No.2, 95-105 http://genbreedpublisher.com/index.php/tgmb 101 Landscape designers and plant scientists need to work closely together to explore the aesthetic characteristics and adaptive abilities of different tree species through scientific research. This includes a comprehensive evaluation of tree morphological features, growth habits, stress tolerance abilities, etc. Additionally, considering the diversity and variability of environmental conditions, the ability of ornamental trees to adapt to future climate change should also be taken into account when selecting and breeding trees. By comprehensively considering the aesthetic traits and adaptive traits of ornamental trees, the aesthetic appeal and practical value of urban greening can be effectively enhanced, promoting the sustainable development of urban green spaces and creating a healthier and more harmonious living environment for urban residents (Tribot et al., 2018). 5 Case Studies of GWAS in Ornamental Tree Genetic Research 5.1 Genetic regulation of flowering duration GWAS has demonstrated great potential in plant genetic research, particularly in revealing genetic factors influencing complex traits such as flowering time. For instance, in the soybean genome, GWAS has revealed key genetic information associated with flowering duration. Kim et al. (2020) studied the genomic structure of 2 662 soybean cultivars using the 180k Axiom® SoyaSNP array (Figure 2). The research focused on identifying candidate markers associated with flowering time. In this study, they detected 93 single nucleotide polymorphism (SNP) markers significantly associated with flowering time, involving 59 important genes, including E1 and E3, which are major determinants of flowering time. Figure 2 Genomic structure of 2 662 cultivated soybean accessions and their relationship among each other (Adopted from Kim et al., 2020) Image caption: a. Phylogenetic tree computed using the identical-by-state coefficient. b. Population structure analysis using the number of genetic clusters (K) ranging from K = 2 to K = 6, based on the maximum likelihood-based clustering algorithm. c. Principal component analysis (PCA) plot of PC1 and PC2 derived using the Kimura two-parameter model. d. PCA plot of PC2 and PC3. e. Extent of linkage disequilibrium (LD) decay computed up to 500 kb. f. Distribution of the flowering time of soybean LRs and ICs. Abbreviations, KN, JN, CN, and OT, indicate accessions collected from Korea, Japan, China, and the other regions, respectively (Adopted from Kim et al., 2020)
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