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基于不同密度SNP芯片在杜洛克公猪中的全基因组选择效果分析 被引量:10

Analysis of Genomic Selection Based on SNP Data of Various Density Chips in Duroc Male Pig Population
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摘要 基因组选择(GS)是近些年发展起来的一项新型育种技术,目前已在动植物育种实践中应用。本研究通过在1068头杜洛克公猪群体中使用不同密度的SNP芯片进行全基因组选择效果比较分析。结果发现:使用基因型填充后芯片以及高密度SNP芯片所获得的估计基因组育种值(GEBV)之间可以达到99%的相关,并发现个体间亲缘关系的远近对同群体内基因型填充结果的准确率影响不大。由此可见,与目标性状紧密相关的低密度SNP芯片可用于实际育种工作,在降低使用成本的同时并不影响全基因组选择效果,为实质性进行猪分子育种提供了一条可行途径。 Genomic selection(GS)is a breeding technique developed in recent years,which now has been applied in animals and plants breeding field.In this study,the GS effects were compared among a male Duroc population consisting of 1068 individuals by using different density SNP chips.The obtained results indicated that genomic estimated breeding values(GEBV)correlation is around 99%between high-density SNP chip group and genotype-imputed chips group.And we also found that the kinship between individuals had little effect on the accuracy of genotype imputing in this population.In summary,the low-density SNP chip closely related to target traits could be used for future pig breeding,which can not only reduce the cost without affecting selection effect,but also provide a feasible way for pig molecular breeding.
作者 王珏 刘成琨 刘德武 王克君 陈洁 吴珍芳 方美英 WANG Jue;LIU Chengkun;LIU Dewu;WANG Kejun;CHEN Jie;WU Zhenfang;FANG Meiying(College of Animal Science and Technology,China Agricultural University,Beijing 100086,China;Berry Genomics Corporation,Beijing 102200,China;College of Animal Science,South China Agricultural University,Guangdong Guangzhou 510642,China;College of Animal Science and Veterinary Medicine,Henan Agricultural University,Henan Zhengzhou 450002,China)
出处 《中国畜牧杂志》 CAS 北大核心 2019年第12期75-79,共5页 Chinese Journal of Animal Science
基金 国家科技重大专项(2018ZX08009-28B)
关键词 全基因组选择 杜洛克公猪 SNP芯片 基因型填充 GEBV Genomic selection Male Duroc SNP chips Genetic imputation GEBV
作者简介 王珏(1991-),男,新疆乌鲁木齐人,博士研究生,研究方向为动物遗传育种与繁殖,E-mail:juewang@cau.edu.cn;通讯作者:方美英(1971-),女,E-mail:meiying@cau.edu.cn。
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  • 1Hayes B, Goddard M. Prediction of total genetic value using genome-wide dense marker maps[J]. Genetics, 2001, 157(4): 1819- 1829.
  • 2Weigel K, Van Tassell C, O'Connell J, et al. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms[J]. J Dairy Sci, 2010, 93(5): 2229.
  • 3Zhang Z, Druet T. Marker imputation with low-density marker panels in Dutch Holstein cattle[J]. J Dairy Sci, 2010, 93(11): 5487- 5494.
  • 4Becker T, Knapp M. Maximum-likelihood estimation of haplotype frequencies in nuclear families [J]. Genet Epidemiol, 2004, 27 (1): 21-32.
  • 5Scheet P, Stephens M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase[J]. Am J Hum Genet, 2006, 78(4): 629-644.
  • 6Li Y, Abecasis G R. Mach 1. 0: rapid haplotype reconstruction and missing genotype inference[J]. Am J Hum Genet S, 2006, 79 (3): 2290.
  • 7Browning S R, Browning B L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering [J]. Am H Hum Genet, 2007, 81(5): 1084.
  • 8Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole -genome association and population -based linkage analyses[J]. Ame J Hum Genet, 2007, 81(3): 559-575.
  • 9Servin B, Stephens M. Imputation-based analysis of association studies: candidate regions and quantitative traits[J]. PLoS Genet, 2007, 3(7): e114.
  • 10Howie B N, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome- wide association studies[J]. PLoS Genet, 2009, 5(6): e1000529.

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