Bioscience Methods 2025, Vol.16, No.6, 299-307 http://bioscipublisher.com/index.php/bm 3 03 involved in the inflammatory response and is related to the rate of tissue regeneration. Factors such as STAT5B and SOCS2, which regulate cytokine signal transduction, also frequently appear on the list of up-regulated expressions in resistant animals. It is worth mentioning that the antioxidant system has not been idle either. Under the background of oxidative stress caused by parasites, some genes that regulate the oxidative state can reduce tissue damage. Ultimately, if these genes can be utilized more effectively in breeding, perhaps the proportion of resistant individuals can be increased without sacrificing tissue integrity. 5 Application of Resistance Gene Markers and Prospects for Molecular Breeding 5.1 Feasibility of marker-assisted selection (MAS) in goat breeding In practical operation, the traditional method of fecal egg counting is indeed not very effective - it is both time-consuming and laborious, and is easily disturbed by various environmental factors, with poor repeatability (Aboshady et al., 2019). As a result, many research and breeding projects began to turn their attention to marker-assisted selection (MAS). As long as genetic markers related to resistance traits, such as SNPS or QTLS, can be identified, the selection of insect-resistant individuals does not necessarily have to wait until adulthood, nor is it as "empirical" as relying solely on phenotypic judgment. Through DNA testing, breeders can pick out potential "good seedlings" even when the animals are still young, which also means that genetic progression will become faster and more stable (Estrada-Reyes et al., 2019). However, don't be too idealistic - compared with sheep, the progress of MAS in goats is still relatively lagging behind. This is mainly due to the lack of detailed genomic resources. Moreover, the trait of resistance itself is complex and is often determined by multiple small-effect genes, which adds a lot of difficulty to the actual breeding operation. 5.2 Challenges in coordinated selection of resistance traits and production performance Sometimes, pest control is not the only goal. Many farmers also pay attention to growth rate, reproductive capacity and milk production, so the question arises: How to choose to have both? Theoretically, there is not much contradiction between these productive traits and insect-resistant traits - some studies even suggest that the genetic correlation between the two is relatively low and they do not affect each other (Heckendorn et al., 2017; Tsukahara et al., 2021). However, the heritability of resistance traits (such as FEC and PCV) is usually at a medium to low level, which means that relying solely on breeding methods to rapidly enhance resistance may not be realistic. One needs to be a little patient. Moreover, in practice, certain negative knock-on effects are inevitable, so the supporting monitoring work cannot be omitted. It is recommended that when making a choice, one should not only consider pest resistance but also incorporate growth and other indicators into a comprehensive selection index model for evaluation. Of course, the entire process from genotyping to phenotypic data collection is not cheap either. For regions with tight resources, it poses a considerable challenge. 5.3 Potential of precision breeding and genomic selection for parasite resistance In the past, when it came to precision breeding, it sounded like an idealized goal. After all, for a long time, tools that could truly "see genes" were not widespread. But now the situation has changed. With the implementation of technologies such as SNP chips, GWAS, and RNA sequencing, even if the sample size is not large, relatively reliable predictive values of resistance traits can be provided (Rocha et al., 2023; Panigrahi et al., 2025). Especially in scenarios where phenotypic judgment cannot always be relied upon, these methods become particularly practical. Of course, in reality, this method cannot be immediately implemented everywhere. One prerequisite is that there must be sufficient genomic resources available, and it would be best if the cost could be even lower. Only when these conditions gradually mature can genomic selection truly enter ordinary livestock farms, rather than just remaining in the laboratories of scientific research institutions and universities. Some people may be worried that pulling multiple traits into one model might make it more complicated. In fact, it is precisely because indicators such as pest resistance, milk production and growth rate can be considered together that comprehensive improvement can be achieved without sacrificing any one of them. This multi-objective breeding strategy sounds easy, but to truly implement it, it still requires continuous advancement in three aspects: tools, costs, and data.
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