Molecular Entomology 2024, Vol.15, No.1, 8-17 http://emtoscipublisher.com/index.php/me 12 challenges and limitations, including the effects of sample size, genetic diversity, environmental factors, and the difficulties of detecting rare variants and complex traits, as well as issues of data sharing and privacy protection. First, sample size is one of the important determinants of GWAS accuracy. A sufficiently large sample size can improve the statistical efficacy of a study, allowing even small genetic effects to be detected. However, in entomopathogen resistance studies, obtaining large numbers of high-quality samples is often difficult, especially for rare or geographically diverse insect species. Genetic diversity also affects the results of GWAS. Highly diverse genetic backgrounds may mask or obscure important genetic signals, making it difficult to analyze the genetic basis of traits. The influence of environmental factors should not be ignored. Insects live in extremely complex environments, and environmental variables such as temperature, humidity, and food sources may affect their resistance to pathogens. Even under the same genetic background, different environmental conditions may lead to significant differences in insect resistance. Failure to adequately control these environmental factors in GWAS studies may lead to an increase in false positive results and affect the accuracy of the study. 3.2 Difficulties in detecting rare variants and complex traits A major challenge in genome-wide association studies (GWAS) is the detection of rare variants and complex traits. These two problems center on the fact that, on the one hand, rare variants are difficult to be effectively detected in conventional GWAS sample sizes due to their extremely low frequency in populations; on the other hand, complex traits are usually the result of multiple genes as well as environmental factors, which makes it particularly difficult to accurately identify all the relevant genetic factors (Du et al., 2021). Rare variants, despite their low frequency, may in some cases have a decisive impact on pathogen resistance in insects. For example, a rare variant may make an insect highly resistant to a specific pathogen, but because the frequency of such variants in a population is extremely low, it is difficult for conventional GWAS designs to be statistically efficacious enough to detect these rare variants that are significantly correlated with traits. Detection of rare variants is also affected by sample selection and genotype quality control criteria, which further increases the difficulty of detection. For the detection of complex traits, the complexity of the polygenic genetic mechanisms and gene-environment interactions underlying the trait are involved. Insect resistance traits are often not simple genetic traits determined by a single gene, but are the result of the interaction of multiple genes under specific environmental conditions. In this case, even if GWAS are able to identify some genetic markers associated with the trait, it is difficult to fully explain the genetic variation in the trait, especially when the trait is strongly influenced by the environment. Interactions between genes (phenotypically non-additive effects) and between genes and the environment also pose challenges in identifying relevant genetic factors. 3.3 Challenges of data sharing and privacy protection Data sharing is an important aspect of accelerating scientific discovery and technological advancement when conducting genome-wide association studies (GWAS). By sharing data, researchers can validate the results of other studies, discover new research directions, or improve statistical validity through meta-analysis. However, the process of data sharing also faces the challenge of privacy protection, and although individual privacy issues may not be as prominent in insect research as in human research, a range of privacy and sensitive information protection issues are still involved. In order to address these challenges, it is crucial to develop sound data management and sharing policies. For example, the legality and legitimacy of the purpose of data use can be ensured through the establishment of a data access committee (DAC) to scrutinize data access requests. De-identify or anonymize data through technical means to reduce the risk of exposing sensitive information. The establishment of clear data use agreements and
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