Tree Genetics and Molecular Breeding 2024, Vol.14, No.2, 95-105 http://genbreedpublisher.com/index.php/tgmb 96 This study aims to explore the genetic basis of aesthetic and adaptive traits in ornamental trees by analyzing their genetic diversity and interactions with environmental factors. This research is expected to provide more scientific guidance for the selection and breeding of ornamental trees. Furthermore, the genetic information revealed by GWAS can support the precise breeding of ornamental trees, making the breeding process more efficient and goal-oriented, thereby accelerating the development and promotion of new varieties. Through these efforts, we can anticipate that future urban greening will not only provide a more vibrant visual experience but also offer stronger ecological services, creating a healthier and more harmonious living environment for urban residents. 2 Overview of GWAS Technology Genome-wide association studies (GWAS) is a powerful tool in plant genetic research, capable of revealing associations between genetic variations and complex traits without prior assumptions. Through GWAS, researchers can discover new trait-related genes, providing scientific guidance for plant breeding and genetic improvement (Uffelmann et al., 2021). Despite facing challenges such as large sample requirements and complex data interpretation, when combined with other techniques and methods, GWAS is expected to continue playing an important role in the field of plant science, particularly in unraveling the genetic basis of complex traits and driving plant genetic improvement. 2.1 Basic principles and methods of GWAS GWAS is a method used to identify associations between genotypes (genetic variations) and phenotypes (observed traits or disease states). This technique is based on a core premise: specific genetic variations (such as single nucleotide polymorphisms, SNPs) may influence an individual's expression of certain traits. GWAS scans thousands of SNPs across the entire genome to find genetic markers associated with specific traits. The implementation of GWAS typically involves the following steps: selecting a sufficiently large sample population and sequencing their genomes to obtain genetic variation data; then, measuring the phenotypic characteristics of individuals in the same population and recording relevant trait data; controlling the quality of genotype and phenotype data, filtering high-quality SNPs and reliable phenotypic data, and handling missing data and outliers; determining the genetic structure of the sample population and the relatedness between individuals to avoid false-positive associations in subsequent analyses; finally, using statistical models to analyze the association between genotype and phenotype, identifying genetic markers significantly associated with specific traits, and performing bioinformatics analysis on the significant SNPs to interpret their potential biological functions and validate them through further experimental research. This association analysis can reveal which gene regions may have an important influence on trait expression (Visscher et al., 2017). 2.2 Applications of GWAS in plant genetic research GWAS has become an important tool for understanding the genetic basis in the field of plant science, particularly in elucidating the genetic mechanisms of traits in crops and trees. It allows researchers to identify genetic markers associated with important agronomic traits, ecological adaptability, and disease resistance across different species. GWAS has been widely used to identify genetic variations related to crop yield, plant growth rate, root development, flowering time control, and seed quality. Pang et al. (2021) used GWAS analysis in wheat to discover quantitative trait loci (QTLs) associated with disease resistance and cold tolerance, and identified candidate genes through high-resolution genetic analysis, which is significant for wheat genetic breeding. Another GWAS study in rice discovered genes and genetic loci strongly associated with yield under salt stress, providing valuable resources for the breeding of salt-tolerant rice varieties (Liu et al., 2019). In Arabidopsis thaliana, researchers integrated geographic and climatic data with genomic information to identify adaptive genetic variations associated with environmental variables, and through GWAS, found the AGG3 gene related to cold tolerance (Ferrero-Serrano and Assmann, 2019). By analyzing the associations between genetic variations and plant phenotypes under different environmental conditions, scientists can identify genes involved in plant environmental adaptation and stress tolerance.
RkJQdWJsaXNoZXIy MjQ4ODYzMg==