Triticeae Genomics and Genetics, 2024, Vol.15, No.1, 19-30 http://cropscipublisher.com/index.php/tgg 27 varieties exhibit better drought tolerance, which is often attributed to the interaction of specific genetic variants with environmental stress. By identifying these key genetic markers, breeders can develop crop varieties better suited to drought conditions. 3.2 Overcoming limitations in GWAS studies Genome-wide association studies (GWAS) have become a powerful tool to reveal the genetic basis behind complex traits, but there are also some limitations in the research process. These limitations include, but are not limited to, multiple testing issues, genetic heterogeneity, neglect of environmental factors, difficulty in detecting rare variants, and the presence of “genetic dark matter.” In response to these challenges, the scientific community has begun to adopt multiple strategies to overcome or mitigate the limitations of GWAS to improve its efficiency and accuracy in revealing complex genetic traits. GWAS involves the simultaneous testing of thousands of single nucleotide polymorphisms (SNPs), which introduces the risk of false positives. To control this risk, researchers used strict statistical correction methods, such as Bonferroni correction and false discovery rate (FDR) control, to adjust the significance threshold. In addition, there are more advanced statistical methods, such as Bayesian methods and mixture models, that can handle multiple comparison problems more efficiently while taking into account the influence of genetic background. Genetic heterogeneity refers to the phenomenon that different genetic variants lead to the same phenotype in different individuals, which brings challenges to the interpretation of GWAS. To address this issue, researchers began performing stratified GWAS analyzes to group samples based on factors such as genetic background, subtypes of phenotypes, or environmental exposures. In addition, the use of meta-analysis methods that integrate data from multiple independent cohorts can improve statistical power and thereby identify common and specific genetic markers in different populations (Qaseem et al., 2017). GWAS usually focus on the influence of genetic factors on phenotype and ignore the role of environmental factors. In order to overcome this limitation, some studies have begun to use environment and gene-environment interaction GWAS (G×E GWAS) to consider both genetic and environmental factors in the analysis. In this way, researchers can gain a more complete understanding of the mechanisms by which phenotypes are formed, especially for traits where environmental factors play an important role. GWAS have traditionally focused on detecting more frequent variants and have less ability to detect rare variants. With the development of next-generation sequencing technology, whole-genome sequencing (WGS) and whole-exome sequencing (WES) provide the possibility to detect rare variants. These techniques allow researchers to identify rare variants genome-wide and assess their contribution to complex traits. Even in successful GWAS, often only a small portion of the contribution of genetic variation to the phenotype can be explained, which is known as the "genetic dark matter" problem. To uncover this hidden genetic information, scientists have used a variety of strategies, including expanding sample sizes, using more complex genetic models, and exploring epigenetic variations and changes in gene expression levels. In addition, by integrating GWAS data with other large-scale bioinformatics databases, such as gene expression maps and protein interaction networks, researchers can discover new genetic-phenotype associations to gain a deeper understanding of the nature of genetic dark matter. 3.3 Future directions and research priorities Although GWAS has successfully identified many markers related to important agronomic traits, many results still require further refined mapping and functional verification. Future research will focus on narrowing down the candidate gene region and determining the specific functional genes and their mechanisms of action through cell and molecular biology experiments. In addition, it will become the norm to use gene editing technologies such as CRISPR/Cas9 to verify the functions of candidate genes discovered by GWAS, which will help deepen our understanding of the genetic basis of wheat crops.
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