Bioscience Methods 2025, Vol.16, No.6, 299-307 http://bioscipublisher.com/index.php/bm 3 01 Figure 1 The change in EPG and immune gene expression. (A) Pipeline for case and control group processing and analysis; (B) EPG in goats infected with H. contortus at different times; (C) qPCR detection of immune gene expression in goat PBMCs at various time points postinfection with H. contortus (*, p < 0.05; **, p < 0.01) (Adopted from Wang et al., 2024) 3 Types and Screening Methods of Genetic Markers 3.1 Application of microsatellites (SSRs) and single nucleotide polymorphisms (SNPs) in resistance studies When conducting resistance research, many researchers would start with SSR in the early stage. This kind of marker itself has high polymorphism and is relatively intuitive to identify. It is often used to evaluate the genetic structure of goat populations or for QTL mapping (Durigan et al., 2018). However, if more precise typing or large sample analysis is required, SNP is clearly more suitable. It is the most common type of variation in the genome and can be rapidly detected by high-throughput platforms, with much higher resolution. For instance, in studies related to egg release, SNP variations in cytokine genes such as IL2, IL13 and IFNG have been identified, which are associated with resistance. The SNPS within the β -tubulin isotype -1 gene of the parasites themselves, such as the Trichoderma nematode, have also been used to assess the level of drug resistance (Huang and Hong, 2025). In fact, whether it is the host or the parasite, the role of SNPS is not limited to localization, but extends to resistance monitoring and mechanism analysis. 3.2 Quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) Not all resistance traits are easy to identify; some are very complex. At this point, QTL mapping comes in handy. Although indicators such as FEC and PCV are measurable, the distribution of gene loci controlling them is scattered. QTL scanning using SSR or SNP markers can preliminarily delineate the relevant regions (Kalaldeh et al., 2019). However, in the past, positioning relying on microsatellites was a bit crude. Later, with the maturation of SNP chip technology, positioning accuracy improved, and it was also possible to identify those QTLS that contributed little individually. In contrast, GWAS places more emphasis on statistical methods and typically seeks the association between SNPS and resistance within multiple varieties and large populations. Genes such as CD79A and MAP3K7 in sheep were discovered in GWAS (Arzick et al., 2025; Costa et al., 2025). Of course,
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