Availability of single nucleotide polymorphism (SNP) genotyping arrays and progress in statistical analyses have allowed the identification of genomic regions and genes under selection in chicken. In this study, SNP data from the 600 K Affymetrix chicken array were used to detect signatures of selection in 23 local Italian chicken populations. The populations were categorized into four groups for comparative analysis based on live weight (heavy vs light) and geographical area (Northern vs Southern Italy). Putative signatures of selection were investigated by combining three extended haplotype homozygosity (EHH) statistical approaches to quantify excess of haplotype homozygosity within (iHS) and between (Rsb and XP-EHH) groups. Presence of runs of homozygosity (ROH) islands was also analysed for each group. After editing, 541 animals and 313,508 SNPs were available for statistical analyses. In total, 15 candidate genomic regions that are potentially under selection were detected among the four groups: eight within a group by iHS and seven by combining the results of Rsb and XP-EHH, which revealed divergent selection between the groups. The largest overlap between genomic regions identified to be under selection by the three approaches was on chicken chromosome 8. Twenty-one genomic regions were identified with the ROH approach but none of these overlapped with regions identified with the three EHH-derived statistics. Some of the identified regions under selection contained candidate genes with biological functions related to environmental stress, immune responses, and disease resistance, which indicate local adaptation of these chicken populations. Compared to commercial lines, local populations are predominantly reared as backyard chickens, and thus, may have developed stronger resistance to environmental challenges. Our results indicate that selection can play an important role in shaping signatures of selection in local chicken populations and can be a starting point to identify gene mutations that could have a useful role with respect to climate change.

中文翻译：

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使用高密度阵列对选择特征进行全基因组映射，确定了鸡生长性状和局部适应的候选基因

单核苷酸多态性 (SNP) 基因分型阵列的可用性和统计分析的进步使得能够鉴定鸡中选择的基因组区域和基因。在这项研究中，来自 600 K Affymetrix 鸡阵列的 SNP 数据用于检测 23 个意大利当地鸡种群的选择特征。根据活重（重与轻）和地理区域（意大利北部与南部）将人群分为四组进行比较分析。通过结合三种扩展单倍型纯合性 (EHH) 统计方法来研究选择的假定特征，以量化 (iHS) 和组间（Rsb 和 XP-EHH）组之间的单倍型纯合性过量。还分析了每组的纯合子运行 (ROH) 岛的存在。编辑后，541只动物和313只，508 个 SNP 可用于统计分析。总共在四个组中检测到 15 个可能处于选择中的候选基因组区域：iHS 在一个组中检测到 8 个，通过结合 Rsb 和 XP-EHH 的结果检测到 7 个，这揭示了组之间的不同选择。三种方法确定的基因组区域之间最大的重叠是在鸡 8 号染色体上。使用 ROH 方法确定了 21 个基因组区域，但没有一个与使用三个 EHH 派生统计数据确定的区域重叠。一些已确定的选择区域包含具有与环境压力、免疫反应和抗病性相关的生物学功能的候选基因，这表明这些鸡种群具有局部适应性。与商业线路相比，当地人口主要作为后院养鸡饲养，因此可能对环境挑战产生了更强的抵抗力。我们的结果表明，选择可以在塑造当地鸡种群的选择特征方面发挥重要作用，并且可以作为识别基因突变的起点，这些突变可能对气候变化具有有用的作用。