Liu Yuqiang, Xu Yang, Li Guangzhen, Ayalew Wondossen, Zhong Zhanming, Zhang Zhe
State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
Animals (Basel). 2025 Aug 18;15(16):2412. doi: 10.3390/ani15162412.
Local adaptation allows animal populations to persist in diverse and changing environments, yet its genomic underpinnings remain poorly characterized in livestock. Chinese indigenous pigs, renowned for their rich phenotypic and ecological diversity, offer a powerful model for investigating environmental adaptation. Here, we integrated whole-genome resequencing data, environmental variables, genotype-environment association (GEA) analyses, and functional annotation to explore the adaptive genomic landscape of 46 native pig breeds across China. Based on 578 individuals and 17.7 million SNPs, we performed genome-wide GEA using latent factor mixed models (LFMMs), identifying 8644 SNPs significantly associated with environmental factors, including 310 linked to precipitation in the wettest quarter (BIO16). Redundancy analysis (RDA) and gradient forest modeling identified BIO16 as a major environmental driver of genomic variation. Functional annotation of BIO16-associated SNPs revealed significant enrichment in regulatory elements and genes highly expressed in the lung, spleen, hypothalamus, and intestine, implicating immune and metabolic pathways in local adaptation. Among the candidate loci, MS4A7 exhibited strong association signals, population differentiation, and tissue-specific regulation, suggesting a role in precipitation-mediated adaptation. This work enhances our understanding of livestock adaptation and informs climate-resilient conservation and breeding strategies.
局部适应使动物种群能够在多样且不断变化的环境中持续存在,但其在牲畜中的基因组基础仍未得到充分表征。中国本土猪以其丰富的表型和生态多样性而闻名,为研究环境适应提供了一个有力的模型。在这里,我们整合了全基因组重测序数据、环境变量、基因型 - 环境关联(GEA)分析和功能注释,以探索中国46个本土猪品种的适应性基因组景观。基于578个个体和1770万个单核苷酸多态性(SNP),我们使用潜在因子混合模型(LFMM)进行全基因组GEA分析,鉴定出8644个与环境因素显著相关的SNP,其中310个与最湿润季度的降水量(BIO16)相关。冗余分析(RDA)和梯度森林建模确定BIO16是基因组变异的主要环境驱动因素。对与BIO16相关的SNP进行功能注释,发现其在调节元件和在肺、脾、下丘脑和肠道中高表达的基因中显著富集,这表明免疫和代谢途径参与了局部适应。在候选基因座中,MS4A7表现出强烈的关联信号、群体分化和组织特异性调控,表明其在降水介导的适应中发挥作用。这项工作增进了我们对牲畜适应性的理解,并为适应气候变化的保护和育种策略提供了依据。