Animal Breeding and Genetics Group, Department of Animal Sciences, Georg-August-University Göttingen, Göttingen, Germany.
LOHMANN Tierzucht GMBH, Cuxhaven, Germany.
PLoS One. 2014 Apr 16;9(4):e94509. doi: 10.1371/journal.pone.0094509. eCollection 2014.
Identifying signatures of selection can provide valuable insight about the genes or genomic regions that are or have been under selective pressure, which can lead to a better understanding of genotype-phenotype relationships. A common strategy for selection signature detection is to compare samples from several populations and search for genomic regions with outstanding genetic differentiation. Wright's fixation index, FST, is a useful index for evaluation of genetic differentiation between populations. The aim of this study was to detect selective signatures between different chicken groups based on SNP-wise FST calculation. A total of 96 individuals of three commercial layer breeds and 14 non-commercial fancy breeds were genotyped with three different 600K SNP-chips. After filtering a total of 1 million SNPs were available for FST calculation. Averages of FST values were calculated for overlapping windows. Comparisons of these were then conducted between commercial egg layers and non-commercial fancy breeds, as well as between white egg layers and brown egg layers. Comparing non-commercial and commercial breeds resulted in the detection of 630 selective signatures, while 656 selective signatures were detected in the comparison between the commercial egg-layer breeds. Annotation of selection signature regions revealed various genes corresponding to productions traits, for which layer breeds were selected. Among them were NCOA1, SREBF2 and RALGAPA1 associated with reproductive traits, broodiness and egg production. Furthermore, several of the detected genes were associated with growth and carcass traits, including POMC, PRKAB2, SPP1, IGF2, CAPN1, TGFb2 and IGFBP2. Our approach demonstrates that including different populations with a specific breeding history can provide a unique opportunity for a better understanding of farm animal selection.
识别选择信号可以提供有关基因或基因组区域的有价值的信息,这些基因或基因组区域正在或曾经受到选择压力的影响,这可以帮助我们更好地理解基因型-表型关系。检测选择信号的一种常见策略是比较来自多个群体的样本,并搜索具有突出遗传分化的基因组区域。Wright 的固定指数,FST,是评估群体间遗传分化的有用指标。本研究旨在基于 SNP 层面的 FST 计算,检测不同鸡群之间的选择信号。共对 3 个商业蛋鸡品种和 14 个非商业观赏鸡品种的 96 个个体进行了基因分型,使用了 3 种不同的 600K SNP 芯片。经过过滤,共有 100 万个 SNP 可用于 FST 计算。为重叠窗口计算 FST 值的平均值。然后在商业蛋鸡和非商业观赏鸡之间以及白壳蛋鸡和褐壳蛋鸡之间进行这些比较。比较非商业和商业品种导致检测到 630 个选择信号,而在商业蛋鸡品种之间的比较中检测到 656 个选择信号。选择信号区域的注释揭示了各种与生产性状相关的基因,这些基因是蛋鸡品种选择的目标。其中包括与繁殖性状、抱窝性和产蛋量相关的 NCOA1、SREBF2 和 RALGAPA1。此外,检测到的几个基因与生长和胴体性状相关,包括 POMC、PRKAB2、SPP1、IGF2、CAPN1、TGFb2 和 IGFBP2。我们的方法表明,包括具有特定育种历史的不同群体可以为更好地理解农场动物的选择提供独特的机会。