Additional file 1 of Genome-environment association analysis reveals climate-driven adaptation of chickens

Additional file 1: Table S1. Summary of samples included in this study. Table S2. Summary of climatic variables included in this study. Table S3. Summary of genome sequencing and mapping statistic. Table S4. Details of SNPs' repartition across the autosomal chromosomes. Table S5. Within-population genetic diversity. Table S6. 1464 candidate SNPs associated with climatic variables as identified by the LFMM approach. Table S7. 5552 candidate SNPs associated with climatic variables as identified by the Samβada. Table S8. 12,584 candidate SNPs associated with climatic variables as identified by the RDA. Table S9. 184 genes identified by the LFMM, individual-based spatial analysis, and RDA. Table S10. The overlap of candidante genes with reported QTLs related to the adaptation of chickens. Table S11. 82 genes associated with temperature adaptation identified by the LFMM, individual-based spatial analysis, and RDA. Table S12. 62 genes associated with precipitation adaptation identified by the LFMM, individual-based spatial analysis, and RDA.

附加文件1：表S1。本研究纳入样本汇总表。表S2。本研究纳入气候变量汇总表。表S3。基因组测序与比对统计结果汇总表。表S4。常染色体上单核苷酸多态性（Single Nucleotide Polymorphisms，SNPs）的分布详情。表S5。群体内遗传多样性。表S6。通过潜在因子混合模型（Latent Factor Mixed Models，LFMM）方法鉴定得到的1464个与气候变量相关的候选单核苷酸多态性位点。表S7。通过Samβada软件鉴定得到的5552个与气候变量相关的候选单核苷酸多态性位点。表S8。通过冗余分析（Redundancy Analysis，RDA）鉴定得到的12584个与气候变量相关的候选单核苷酸多态性位点。表S9。通过LFMM、基于个体的空间分析以及RDA三种方法共同鉴定得到的184个基因。表S10。候选基因与已报道的鸡适应性相关数量性状位点（Quantitative Trait Locus，QTL）的重叠情况。表S11。通过LFMM、基于个体的空间分析以及RDA三种方法共同鉴定得到的82个与温度适应相关的基因。表S12。通过LFMM、基于个体的空间分析以及RDA三种方法共同鉴定得到的62个与降水适应相关的基因。