| 利用猪1.4M高密度SNP芯片检测巴马香猪全基因组拷贝数变异 |
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| 引用本文： | 邱恒清,肖石军,郭源梅.利用猪1.4M高密度SNP芯片检测巴马香猪全基因组拷贝数变异[J].畜牧兽医学报,2020,51(9):2079-2088. |
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| 作者姓名： | 邱恒清 肖石军 郭源梅 |
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| 作者单位： | 1. 江西农业大学 省部共建种猪遗传改良与养殖技术国家重点实验室, 南昌 330045;2. 江西省吉安市畜牧兽医局, 吉安 343000 |
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| 基金项目： | 国家自然科学基金（31972542；31660303） |
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| 摘 要： | 旨在检测巴马香猪基因组上的拷贝数变异（CNV），并探究标记密度对于CNV检测效率和准确率的影响。本研究利用319头巴马香猪（其中阉公猪160头和母猪159头）1.4M高密度SNP芯片的数据，采用PennCNV和R-Gada两种软件进行CNVs检测；然后通过重叠CNV融合法，构建拷贝数变异区域（CNVR），并用全基因组关联分析（GWAS）对频率大于5%的CNVR进行验证；最后根据不同的标记密度，均匀抽取一定数目的SNPs来探究标记密度对CNV检测效率和准确性的影响。结果，PennCNV和R-Gada软件分别检测到6 327和3 489个CNVs，分别构成795和340个CNVRs，其中226个为共同CNVRs。在这226个共同CNVRs中，最短的为3.98 kb，最长的为1 297.78 kb，总长度为33.27 Mb，其中102个（45%）与前人报道的CNVRs重叠。在PennCNV检出的795个CNVRs中，有135个频率大于5%，其中20个得到GWAS验证，验证率为15%。随着SNP密度的逐渐增加，CNV的检测效率和检测准确性不断提高，尤其是小片段CNVs的检测效率。本研究利用1.4M SNP芯片的数据，通过PennCNV和R-Gada软件绘制巴马香猪CNVR的草图，为将来鉴别与重要经济性状相关的CNVRs奠定了基础。同时，揭示了标记密度对CNV检测效率和准确性有正面影响，为后续CNV研究选择合适的标记密度提供了一定的参考。
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| 关 键 词： | 巴马香猪 拷贝数变异 1.4M高密度SNP芯片 |
| 收稿时间： | 2020-01-31 |
Detection of Genome-wide Copy Number Variation Using Porcine 1.4M High-density SNP Chips in Bama Xiang Pigs |
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| QIU Hengqing,XIAO Shijun,GUO Yuanmei.Detection of Genome-wide Copy Number Variation Using Porcine 1.4M High-density SNP Chips in Bama Xiang Pigs[J].Acta Veterinaria et Zootechnica Sinica,2020,51(9):2079-2088. |
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| Authors: | QIU Hengqing XIAO Shijun GUO Yuanmei |
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| Institution: | 1. State Key Laboratory of Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang 330045, China;2. Animal Husbandry and Veterinary Bureau of Ji'an City in Jiangxi Province, Ji'an 343000, China |
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| Abstract: | The aim of this study was to detect the copy number variation (CNV) in the genome of Bama Xiang pigs and investigate the effect of marker density on the efficiency and accuracy of CNV detection. PennCNV and R-Gada were employed to detect CNVs using the 1.4M high-density SNP chip data of 319 (160 hogs and 159 gilts) Bama Xiang pigs, and the CNV region (CNVR) was constructed by merging overlapping CNVs. Only the CNVR with higher frequency than 5% was verified by the genome-wide association study (GWAS). Finally, according to the marker densities, a certain number of SNPs were evenly extracted, and the effect of marker density on CNV detection efficiency and accuracy was explored. There were 6 327 CNVs detected by PennCNV and 3 489 CNVs detected by R-Gada, which made up of 795 and 340 CNVRs, respectively, including 226 CNVRs identified by both programs. Among the 226 CNVRs, the shortest was 3.98 kb, the longest was 1 297.78 kb, and their total length was 33.27 Mb, of which 102 (45%) overlapped the CNVRs reported previously. Among the 795 CNVRs detected by PennCNV, 135 had a higher frequency than 5%, 20 of which had been verified by GWAS, and the verification rate was 15%. With the SNP density increasing, the efficiency and accuracy of CNV detection were increased, especially for the small size CNVs. A CNVR sketch of Bama Xiang pigs had been drawn using 1.4M SNP chips, which was helpful to identify CNVRs associated with important economic traits in the future. At the same time, we revealed the positive effect of marker density on the efficiency and accuracy of CNV detection, and the results provided a reference of choosing marker density for the follow-up research of CNV detection. |
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| Keywords: | Bama Xiang pig copy number variation 1 4M high-density SNP chip |
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