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商业和非商业鸡品种基因组中的选择信号。

Signatures of selection in the genomes of commercial and non-commercial chicken breeds.

机构信息

Animal Breeding and Genomics Centre, Wageningen University and Research Centre, Wageningen, The Netherlands.

出版信息

PLoS One. 2012;7(2):e32720. doi: 10.1371/journal.pone.0032720. Epub 2012 Feb 27.

Abstract

Identifying genomics regions that are affected by selection is important to understand the domestication and selection history of the domesticated chicken, as well as understanding molecular pathways underlying phenotypic traits and breeding goals. While whole-genome approaches, either high-density SNP chips or massively parallel sequencing, have been successfully applied to identify evidence for selective sweeps in chicken, it has been difficult to distinguish patterns of selection and stochastic and breed specific effects. Here we present a study to identify selective sweeps in a large number of chicken breeds (67 in total) using a high-density (58 K) SNP chip. We analyzed commercial chickens representing all major breeding goals. In addition, we analyzed non-commercial chicken diversity for almost all recognized traditional Dutch breeds and a selection of representative breeds from China. Based on their shared history or breeding goal we in silico grouped the breeds into 14 breed groups. We identified 396 chromosomal regions that show suggestive evidence of selection in at least one breed group with 26 of these regions showing strong evidence of selection. Of these 26 regions, 13 were previously described and 13 yield new candidate genes for performance traits in chicken. Our approach demonstrates the strength of including many different populations with similar, and breed groups with different selection histories to reduce stochastic effects based on single populations.

摘要

确定受选择影响的基因组区域对于理解家鸡的驯化和选择历史以及理解表型特征和育种目标的分子途径非常重要。虽然全基因组方法,无论是高密度 SNP 芯片还是大规模平行测序,都已成功应用于识别鸡中的选择清除证据,但很难区分选择模式与随机和特定品种的影响。在这里,我们使用高密度(58K)SNP 芯片对大量鸡品种(共 67 个)进行了选择性清扫的研究。我们分析了代表所有主要育种目标的商业鸡。此外,我们还分析了几乎所有公认的传统荷兰品种的非商业鸡多样性,以及来自中国的代表性品种的选择。根据它们的共同历史或育种目标,我们在计算机上将这些品种分为 14 个品种组。我们在至少一个品种组中鉴定出 396 个染色体区域,这些区域在至少一个品种组中显示出选择的明显证据,其中 26 个区域显示出选择的强烈证据。在这 26 个区域中,有 13 个是先前描述的,13 个产生了鸡性能特征的新候选基因。我们的方法证明了包括具有相似历史的许多不同群体和具有不同选择历史的品种组的力量,以基于单个群体减少随机效应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/720c/3287981/268b7e44a943/pone.0032720.g001.jpg

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