Molecular Plant Breeding 2024, Vol.15, No.3, 132-143 http://genbreedpublisher.com/index.php/mpb 137 Figure 3 Realized genomic predictive abilities for mean annual increment (MAI) in tree growth at ages 3 and 6 years (Adopted from Simiqueli et al., 2023) Image caption: estimated with an additive (GBLUP_G), an additive + dominance (GBLUP_G+D) model, and an additive HBLUP model, trained with different training datasets (Adopted from Simiqueli et al., 2023) 3.3.3 Case 3: growth improvement in rubber Francisco et al. (2021) conducted an in-depth exploration of the genetic mechanisms underlying rubber tree growth using a multi-omics approach, including genome-wide association studies (GWAS) and RNA sequencing. The research team employed molecular marker-assisted selection (MAS) techniques to identify key genes and molecular markers that influence rubber tree growth and yield, which are closely associated with important agronomic traits such as growth rate and stress resistance (Figure 4). By establishing gene co-expression networks and enzyme network models, the study revealed the molecular networks involved in the phenotypic formation of rubber trees, providing new insights into gene function. These findings not only aid scientists in screening rubber trees with superior genetic traits and accelerating the breeding process but also enable precise genetic improvement of rubber trees using MAS techniques. This includes enhancing rubber yield and improving rubber quality. The outcomes of this study are significant for the genetic improvement of rubber trees, allowing breeders to select and cultivate high-yield and high-quality rubber tree varieties that are well-adapted to different environmental conditions in a shorter time frame. This approach can effectively increase the production efficiency and economic value of rubber trees, positively impacting the sustainable development of the rubber industry. 4 Applications of Marker-Assisted Selection in Tree Breeding The application of marker-assisted selection in tree breeding has revolutionized the process of developing disease-resistant, fast-growing, high-yielding, and superior wood quality tree varieties. The integration of advanced genomic tools and techniques continues to enhance the efficiency and precision of tree breeding programs, ensuring the sustainable management and conservation of forest resources.
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