| 基于简化基因组测序技术和基因芯片技术比较研究黄羽肉鸡基因组选择 |
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| 引用本文： | 刘天飞,罗成龙,王艳,周广源,马杰,舒鼎铭,苏国生,瞿浩.基于简化基因组测序技术和基因芯片技术比较研究黄羽肉鸡基因组选择[J].畜牧兽医学报,2020,51(10):2378-2386. |
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| 作者姓名： | 刘天飞 罗成龙 王艳 周广源 马杰 舒鼎铭 苏国生 瞿浩 |
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| 作者单位： | 1. 广东省农业科学院动物科学研究所, 畜禽育种国家重点实验室, 广东省畜禽育种与营养研究重点实验室, 广州 510640;2. 佛山科学技术学院生命科学与工程学院, 佛山 528231;3. 丹麦奥胡斯大学分子生物学和遗传学系, Tjele DK-8830 |
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| 基金项目： | 广东省科技计划项目（2017B020201006；2017B020232003）；广东省自然科学基金（2020A1515011277）；现代产业技术体系岗位科学家专项（CARS-41）；广东省现代农业产业技术体系创新团队（2019KJ128）；科技创新战略专项资金—高水平农科院建设（R2017PY-QY007） |
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| 摘 要： | 旨在比较简化基因组测序技术和基因芯片技术实施基因组选择的基因组估计育种值（GEBV）准确性。本研究在AH肉鸡资源群体F2代中随机选取395个个体（其中公鸡212只，母鸡183只，来自8个半同胞家系），同时采用10×SLAF测序技术和Illumina Chicken 60K SNP芯片进行基因标记分型。采用基因组最佳无偏估计法（GBLUP）和BayesCπ对6周体重、12周体重、日均增重、日均采食量、饲料转化率和剩余采食量等6个性状进行GEBV准确性比较研究，并采用5折交叉验证法验证。结果表明，采用同一基因标记分型平台，两种育种值估计方法所得GEBV准确性差异不显著（P>0.05）；不同的性状对基因标记分型平台的选择存在差异，对于6周体重，使用基因芯片可获得更高的GEBV准确性（P<0.05），对于剩余采食量，则使用简化基因组测序可获得更高的GEBV准确性（P<0.05）。综合6个性状GEBV均值比较，两个基因标记分型平台之间差异不到0.01，高通量测序技术和基因芯片技术都可以用于黄羽肉鸡基因组选择。
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| 关 键 词： | 黄羽肉鸡 基因组估计育种值 简化基因组测序 基因芯片 交叉验证法 |
| 收稿时间： | 2020-04-27 |
Comparison between Reduced-Representation Genome Sequencing and SNP Chip for Genomic Selection in Yellow-feathered Broiler |
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| LIU Tianfei,LUO Chenglong,WANG Yan,ZHOU Guangyuan,MA Jie,SHU Dingming,SU Guosheng,QU Hao.Comparison between Reduced-Representation Genome Sequencing and SNP Chip for Genomic Selection in Yellow-feathered Broiler[J].Acta Veterinaria et Zootechnica Sinica,2020,51(10):2378-2386. |
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| Authors: | LIU Tianfei LUO Chenglong WANG Yan ZHOU Guangyuan MA Jie SHU Dingming SU Guosheng QU Hao |
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| Institution: | 1. Guangdong Provincial Key Laboratory of Animal Breeding and Nutrition, State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China;2. College of Life Science and Engineering, Foshan University of Science and Technology, Foshan 528231, China;3. Department of Molecular Biology and Genetics, Aarhus University, Tjele 8830, Denmark |
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| Abstract: | This study aimed to compare the accuracy of the genomic estimated breeding value (GEBV) using reduced-representation genome sequencing technology and SNP chip technology to implement genomic selection. A total of 395 individuals (212♂+ 183♀, from 8 half-sib families) were randomly selected from F2 generation of AH broiler resource population, and genotyped with 10×specific-locus amplified fragment sequencing (SLAF-seq) and Illumina Chicken 60K SNP BeadChip. Genomic best linear unbiased prediction (GBLUP) and BayesCπ were used to compare the accuracy of genomic estimated breeding values (GEBV) for 6 traits: body weight at the 6th week, body weight at the 12th week, average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR) and residual feed intake (RFI). A 5-fold cross validation procedure was used to verify the accuracies of GEBV between prediction models and between genotyping platforms. The results showed that there was no significant difference between accuracies of GEBV predicted by GBLUP and BayesCπ using the same genotyping platform(P>0.05). The superiority of the two genotyping platforms was different for different traits. For body weight at the 6th week, the accuracy of GEBV was higher using chip SNPs (P<0.05). On the contrary, the accuracy was higher using SLAF-seq for residual feed intake (P<0.05). Comprehensive comparison of the means of GEBV for 6 traits, the difference between the two genotyping platforms was less than 0.01, therefore, both high throughput sequencing and chip SNPs can be used for genomic selection in yellow-feathered broiler. |
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| Keywords: | yellow-feathered broiler genomic estimated breeding value reduced-representation genomic sequencing SNP chip cross validation |
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