Tree Genetics and Molecular Breeding 2024, Vol.14, No.2, 95-105 http://genbreedpublisher.com/index.php/tgmb 98 2.3 Advantages and limitations of GWAS in revealing the genetic basis of plant traits GWAS offers significant advantages in revealing the genetic basis of plant traits as it can perform genome-wide analysis without prior information about candidate genes, making it a powerful tool for discovering new trait-related genes and genetic variations (Tam et al., 2019). Additionally, GWAS can leverage the genetic diversity present in natural populations, meaning it can uncover genetic variations that have been beneficial for plant survival and reproduction through long-term evolutionary processes. However, GWAS also has limitations. For instance, it requires large sample sizes to ensure the effectiveness and reliability of statistical analyses. GWAS may have difficulty distinguishing between genetic markers tightly linked to a trait and the actual causal variations, as many genetic variations are highly associated. Furthermore, the influence of environmental factors on plant traits may also interfere with GWAS results, especially when considering traits with strong environmental dependence. Sometimes, GWAS results may only point to a larger gene region rather than a specific gene or variation, requiring subsequent research through finer genetic mapping or functional validation experiments to identify the causal gene. Despite these challenges and limitations, the strength of GWAS lies in its ability to systematically evaluate the relationships between genetic variations and traits, providing clues to understand the genetic mechanisms underlying complex traits. To overcome these limitations, researchers often employ multiple strategies, such as increasing sample sizes to improve statistical power, using advanced genomic technologies for fine mapping, or combining GWAS with other genetic and genomic methods (such as linkage analysis, phenomics, and gene expression analysis) to further validate and explore the association signals discovered by GWAS. 3 Data Collection and Analysis Techniques for GWAS in Ornamental Tree Genetic Research In the genetic research of ornamental trees, data collection and analysis techniques for genome-wide association studies (GWAS) are crucial. These techniques not only help researchers gain a deeper understanding of the genetic basis of trees but also provide scientific guidance for tree selection, breeding, and management. Specifically, they include population structure analysis, association analysis, gene localization and functional prediction, and experimental validation. 3.1 Population structure analysis Population structure analysis plays a key role in GWAS by identifying and correcting potential genetic background differences within the sample population. Ignoring population structure may lead to false-positive associations, making a proper understanding and correction of population structure an essential step in GWAS research. Common methods for population structure analysis include principal component analysis (PCA), multidimensional scaling (MDS), and structure equation modeling. PCA is a widely used method for population structure analysis. Its basic principle is to transform the genetic relatedness between samples into a set of orthogonal principal components, which represent most of the variation in the data and can therefore be used to describe the genetic relatedness between samples (van Waaij et al., 2023). Through PCA, researchers can project samples onto the principal component space and infer population structure and kinship relationships by observing the distribution of samples in this space. Multidimensional scaling (MDS) is another commonly used method for population structure analysis. It transforms the genetic distances between samples into a set of coordinates in a low-dimensional space, describing the relative positions of samples. Similar to PCA, MDS helps researchers visualize population structure and identify potential kinship relationships by representing samples in two or three-dimensional space. In addition to PCA and MDS, structure equation modeling is also a commonly used method for population structure analysis. It constructs a genetic similarity matrix between samples and uses maximum likelihood estimation to infer the population structure of the samples. This method can account for complex kinship relationships between samples, improving the accuracy and precision of population structure analysis.
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