Molecular Entomology 2024, Vol.15, No.1, 8-17 http://emtoscipublisher.com/index.php/me 9 Although GWAS shows great potential for insect resistance research, its application faces a number of challenges, including the huge amount of data, computational complexity, and difficulties in interpreting the results. How to translate the genetic information discovered by GWAS into practical pest management strategies is also an important direction of current research (Li et al., 2022). The aim of this study was to overview GWAS-based insect pathogen resistance research, analyze its methodology, challenges and future prospects, introduce the basic principles of GWAS technology and its application in insect resistance research, and discuss in detail the progress of research on the genetic basis of insect resistance, including the key genes and functional regions discovered through GWAS technology. This study explores the major challenges encountered in the implementation of GWAS research, such as the effects of sample size, genetic diversity and environmental factors, etc. It will also demonstrate the application and achievements of GWAS technology in revealing insect resistance mechanisms through several specific research cases. This study will discuss the potential applications and perspectives of GWAS technology in future entomopathogen resistance studies, and how complex problems in entomopathogen resistance studies can be solved through interdisciplinary collaborations. 1 Application of GWAS Technical Methods 1.1 The role of GWAS in insect pathogen resistance research Genome-wide association studies (GWAS) is a scientific method used to study the association between genetic variation and complex traits, and has been widely used in insect pathogen resistance research in recent years.The basic principle of GWAS is to search for genetic markers associated with specific traits by scanning genome-wide genetic variation in a large number of individuals. genetic markers associated with specific traits. This approach can reveal the genetic basis behind traits and provide new ideas for understanding the mechanisms of insect resistance to pathogens. The GWAS technique is based on the premise that differences in traits can to some extent be explained by genetic variation in the genome. These variations usually refer to single nucleotide polymorphisms (SNPs), which are the most common genetic markers in the genome. By comparing the distribution of SNPs in populations of individuals with different traits (e.g., disease resistance versus disease susceptibility), it is possible to identify genetic loci that are significantly associated with a particular trait.GWAS studies usually require a large number of samples in order to ensure the reliability of the statistical results (Liu et al., 2023). The basic steps in conducting a GWAS study include: sample collection, DNA extraction, genotyping, statistical analysis, and validation of candidate loci. Each step requires precise technical support and strict quality control to ensure the accuracy and reliability of the data. 1.2 Special considerations and applicability of GWAS technology in insect research Insects, as research objects, have their own special characteristics when applying GWAS technology. There are many kinds of insects, and the genetic backgrounds of different kinds vary greatly, which requires representativeness and comparability when choosing research objects. Insects have a short life cycle and fast reproduction rate, which facilitates the rapid acquisition of a large amount of genetic material, but also requires researchers to be able to effectively manage and maintain the experimental population. The small size of insects and the limited amount of DNA extracted require the use of highly sensitive genotyping techniques. The application of GWAS techniques in insect resistance research also requires consideration of the impact of environmental factors. Insect resistance traits are often affected by a combination of multiple genetic and environmental factors, which requires the use of appropriate models to control these confounding factors when performing GWAS analysis to ensure the accuracy of the research results.
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