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Genome-Wide Copy Number Variations Inferred from SNP Genotyping Arrays Using a Large White and Minzhu Intercross Population

机译:从SNP基因分型阵列使用大的白色和民朱交叉人口推断出的全基因组拷贝数变异。

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Copy number variations (CNVs) are one of the main contributors to genetic diversity in animals and are broadly distributed in the genomes of swine. Investigating the performance and evolutionary impacts of pig CNVs requires comprehensive knowledge of their structure and function within and between breeds. In the current study, 4 different programs (i.e., GADA, PennCNV, QuantiSNP, and cnvPartition) were used to analyze Porcine SNP60 genotyping data of 585 pigs from one Large White × Minzhu intercross population to detect copy number variant regions (CNVRs). Overlapping CNVRs recalled by at least 2 programs were used to construct a powerful and comprehensive CNVR map, which contained249 CNVRs (i.e., 70 gains, 43 losses, and 136 gains/losses) and covered 26.22% of the regions in the swine genome. Ten CNVRs, representing different predicted statuses, were selected for validation via quantitative real-time PCR (QPCR); 9/10 CNVRs (i.e., 90%) were validated. When being traced back to the F0 generation, 58 events were identified in only Minzhu F0 parents and 2 events were identified in only Large White F0 parents. A series of CNVR function analyses were performed. Some of the CNVRs functions were predicted, and several interesting CNVRs for meat quality traits and hematological parameters were obtained. A comprehensive and lower false rate genome-wide CNV map was constructed for Large White and Minzhu pig genomes in this study. Our results may provide an important basis for determining the relationship between CNVRs and important qualitative and quantitative traits. In addition, it can help to further understand genetic processes in pigs.
机译:拷贝数变异(CNV)是动物遗传多样性的主要贡献者之一,广泛分布于猪的基因组中。要研究猪CNV的性能和进化影响,需要全面了解其在品种内和品种之间的结构和功能。在本研究中,使用4个不同程序(即GADA,PennCNV,QuantiSNP和cnvPartition)分析了来自一个大白×民珠交叉种群的585头猪的猪SNP60基因分型数据,以检测拷贝数变异区(CNVR)。至少有2个程序调用的重叠CNVR用于构建功能强大而全面的CNVR图谱,其中包含249个CNVR(即70个增益,43个丢失和136个增益/损耗),覆盖了猪基因组区域的26.22%。通过定量实时PCR(QPCR)选择了十个代表不同预测状态的CNVR进行验证;验证了9/10个CNVR(即90%)。追溯到F0代时,仅在闽珠F0亲本中鉴定出58个事件,仅在大白F0亲本中鉴定出2个事件。进行了一系列的CNVR功能分析。预测了CNVR的一些功能,并获得了一些有关肉质性状和血液学参数的有趣的CNVR。在这项研究中,为大白猪和民猪的基因组构建了一个综合性且误报率较低的全基因组CNV图。我们的结果可能为确定CNVR与重要的定性和定量性状之间的关系提供重要依据。此外,它可以帮助进一步了解猪的遗传过程。

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