畜牧兽医学报 ›› 2016, Vol. 47 ›› Issue (2): 232-240.doi: 10.11843/j.issn.0366-6964.2016.02.004

• 遗传繁育 • 上一篇    下一篇

白色杜洛克×二花脸资源家系猪血液性状的系统遗传学研究

徐盼,张震,崔磊磊,杨斌,段艳宇*   

  1. (江西农业大学省部共建猪遗传改良与养殖技术国家重点实验室,南昌 330045)
  • 收稿日期:2015-02-16 出版日期:2016-02-23 发布日期:2016-02-23
  • 通讯作者: 段艳宇,博士,讲师,Tel/Fax:0791-83813080,E-mail:yanyuduan@hotmail.com
  • 作者简介:徐盼(1988-),男,江苏南京人,博士生,主要从事动物遗传育种研究,E-mail:panxu_nj@hotmail.com
  • 基金资助:

    国家高技术研究发展计划(863计划)(2013AA102502);江西省科技厅科技支撑项目(2009BNA06900)

A Systems Genetics Study of Hematological Traits in a White Duroc × Erhualian Pigs F2 Resource Population

XU Pan,ZHANG Zhen,CUI Lei-lei,YANG Bin,DUAN Yan-yu*   

  1. (State Key Laboratory for Pig Genetic Improvement and Production Technology,Jiangxi Agricultural University,Nanchang 330045,China)
  • Received:2015-02-16 Online:2016-02-23 Published:2016-02-23

摘要:

为揭示家猪血细胞性状的遗传基础,本研究整合白色杜洛克×二花脸猪F2资源家系肌肉血细胞性状eQTL和全基因关联分析结果,鉴别影响血细胞性状的候选基因。研究测定了1 149个个体的18种血常规和593个个体肌肉转录本表达水平,对1 020个个体进行基因分型。转录组表达水平与血常规数据的关联性使用斯皮尔曼系数进行评估,将血细胞性状相关联的转录本进行eQTL定位并进行基因富集分析,结合前期本试验的GWAS结果进行综合分析。结果,当P<5×10-4时检测到血细胞相关的eQTLs有122个,其中有9个顺式eQTLs和63个反式eQTLs。通过eQTL定位确定SERPINB6为影响红细胞性状的候选基因,基因富集分析鉴别到影响红细胞性状的候选基因为PTPRCITGA8,GWAS和eQTL的综合分析发现影响红细胞性状相关的基因为TMED9、PCK1、TINAGL1和SORL1。本研究通过结合前期GWAS结果和肌肉转录本表达数据进行综合分析鉴别到7个与红细胞性状相关的基因,其中TMED9、PCK1、TINAGL1和SORL1是GWAS和eQTL共有的候选基因。

Abstract:

 To dissect the hereditary basis for hematological traits,we integrated the results of genome-wide association study and muscle expression quantitative loci(eQTL) analysis to identify the candidate genes in a White Duroc×Erhualian pigs F2 resource population on the basis of systems genetics.In this study,the 18 hematological parameters of 1 149 pigs were measured and the expressed transcripts of muscles in 593 pigs were detected,1 020 individuals were genotyped.The correlations between gene expressions and phenotypic data were evaluated using Spearman correlation coefficient.Those eQTL were identified by aligning the related transcripts with the pig reference genome and their ontology enrichment were also implemented.We also performed the integrated analysis by incorporating eQTL information into our blood-based GWAS data.In this study,122 eQTLs were identified including 9 cis-eQTLs and 63 trans-eQTLs with a conservative threshold P<5×10-4.Of them,SERPINB6 was an identified candidate gene by eQTL.PTPRC and ITGA8 were prioritized as candidate genes by gene ontology enrichment analysis.TMED9,PCK1,TINAGL1 and SORL1 were highlighted as the most promising candidate genes by integrative eQTL and GWAS analysis.We identified 7 novel candidate genes by integrating the previous blood-based genome-wide association study and muscle gene expression profiles analysis.PCK1,TMED9,TINAGL1 and SORL1 shared the association signals of GWAS and eQTL.

中图分类号: