Tree Genetics and Molecular Breeding 2024, Vol.14, No.2, 95-105 http://genbreedpublisher.com/index.php/tgmb 97 In disease management research, GWAS has been used to discover genes associated with plant disease resistance. For example, in a soybean breeding collection from South East and South Kazakhstan, GWAS was used to analyze resistance to multiple fungal diseases, identifying QTLs associated with resistance to multiple pathogens, which is valuable for improving local breeding programs (Zatybekov et al., 2018). GWAS can also be used to study gene-environment interactions, i.e., how genes influence phenotypic expression under specific environmental conditions. Beilsmith et al. (2019) discussed early efforts and future prospects of using GWAS in plant leaf microbiome research. They emphasized that GWAS is an increasingly promising approach for identifying plant genetic variations associated with microbial communities. By considering microbes as a community rather than single host-microbe interactions in GWAS analysis, complex plant-microbiome interactions can be revealed (Figure 1). Figure 1 How to use genome-wide association studies (GWAS) with your plant leaf microbiome (Adopted from Beilsmith et al., 2019) Image caption: 1. THE QUESTION: Understanding the leaf microbiome from an evolutionary, mechanistic plant physiological or agricultural perspective requires different downstream considerations. 2. PLANT GENOTYPING: Selecting the plant panel is an essential step in non-model systems. A plant panel with the appropriate diversity and population structure must be selected. 3. DESIGN: One can sample microbiomes from plant leaves in their natural habitat, in a common garden experiment, or in sterile environments like well plates. 4. SAMPLING: Differences in sampling methods often concern surface cleaning treatments and the time during which leaf samples are stored before DNA is isolated. 5. PHENOTYPING: Microbiota can be quantified with amplicon or metagenomic sequencing, and converted into relevant phenotypic traits for GWAS. 6. GWAS: Several statistical models exist to perform GWAS on the leaf microbiome of plants and estimate the significance of associations with microbiome traits at loci across the genome; 7. VALIDATION: Genes underlying candidate loci can be functionally validated through post-GWAS statistical approaches, such as bioinformatic methods that use transcriptome datasets and reverse genetic gene editing methods (Adopted from Beilsmith et al., 2019) Through these applications, GWAS has not only deepened our understanding of plant genetic diversity and the genetic mechanisms of complex traits but also provided valuable molecular markers for plant breeding and genetic improvement. These achievements have practical value in improving crop yield, enhancing quality, increasing disease resistance, and promoting environmental adaptability.
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