International Journal of Molecular Ecology and Conservation, 2025, Vol.15, No.1, 44-53 http://ecoevopublisher.com/index.php/ijmec 47 3.3 Disease resistance and immunity Scientists have also found genes related to immunity and disease resistance, especially in chickens that live in harsh conditions. In chickens living in tropical deserts, some genes, such as TLR7 and ZC3HAV1, can inhibit the replication of viruses, which means that they can better cope with diseases that both humans and animals may get (Tian et al., 2020). Genes related to immune response, antioxidants, and anti-inflammation have also been found in some native chickens in Africa and Asia. These genes may make them more resistant to local pathogens and environmental stresses (Fleming et al., 2016; Xie et al., 2024). These studies show that the immune genes of chickens are very important (Li et al., 2024). It is precisely because of these genetic adaptations that chickens can survive in many different and even harsh environments and maintain a certain level of production capacity. 4 Methodologies in Population Genomic Studies 4.1 Whole genome resequencing and SNP arrays To study the chicken genome, some efficient technologies are generally used, such as whole genome sequencing (WGS) and SNP chips. WGS can scan the entire genome and find a large number of SNPs (small genetic variations) in different chicken populations, including some new variations that have not been discovered before (Li et al., 2017). SNP chips such as Illumina 60K are more cost-effective and suitable for large-scale analysis, which can help us understand the genetic diversity, population structure and relationship between breeds of chickens (Dementieva et al., 2024). There is also a method called GBS (genotyping by sequencing), such as CornellGBS, which is low-cost and efficient. It is particularly suitable for studies with a large number of samples and can quickly find many SNPs (Pértille et al., 2016). If we use multiple reference samples to "complete" some unmeasured sites, we can also make the results more accurate, especially for better identification of some relatively rare genetic variants (Ye et al., 2019). 4.2 Population structure and phylogenetic analyses Sometimes it's hard to tell how chickens are related just by looking at their appearance, after all, some differences are natural, while others are artificially selected. To figure out where these chickens came from and how close they are to each other, researchers will first do some data analysis. For example, they will use some tools to look at the composition and ancestral clues of different chicken groups, such as principal component analysis (PCA) and ADMIXTURE clustering, which are actually quite commonly used. Of course, it doesn't mean that you can draw a conclusion at a glance. Scientists will also analyze the genetic data of many chickens together, especially using genetic markers such as SNPs to draw a phylogenetic tree similar to a "family tree", so that you can see which chicken breeds are from the same branch and which ones have gradually evolved later (Yan et al., 2024). Although the method sounds a bit technical, the purpose is actually very direct, which is to figure out how chickens have gradually become so diverse. These analyses sometimes reveal things that don't seem obvious. For example, some chickens look similar, but they have actually been separated genetically for a long time; while some chickens with big differences may have common ancestors. Behind this, there are both natural evolutionary factors and human breeding selection (Rosenberg et al., 2001). Therefore, these tools and maps are not just for academic research, they also help us understand a larger and more complex story. 4.3 Selection scans and functional annotation To understand how chickens adapt to the environment, we need to find out which gene regions are selected naturally or artificially. Researchers use methods such as iHS, XP-EHH, FST and Pi, which can help us find genes or gene regions related to environmental adaptation (Chen et al., 2011). After finding these candidate genes, we need to further look at what they do specifically. At this time, we will use some annotation tools, such as GO and KEGG pathway analysis, to determine whether these genes are related to the growth, body shape or environmental adaptability of chickens (Bello et al., 2023). Sometimes, we will combine different methods to more accurately find truly useful gene markers and gain a deeper understanding of the genetic mechanism of chicken adaptation (Liang and Xuan, 2024).
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