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一项使用推算全基因组序列数据的关联研究在杜洛克×二花脸F群体中鉴定出与生长相关性状的新显著位点。

An association study using imputed whole-genome sequence data identifies novel significant loci for growth-related traits in a Duroc × Erhualian F population.

作者信息

Ji Jiuxiu, Yan Guorong, Chen Dong, Xiao Shijun, Gao Jun, Zhang Zhiyan

机构信息

State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, China.

出版信息

J Anim Breed Genet. 2019 May;136(3):217-228. doi: 10.1111/jbg.12389. Epub 2019 Mar 14.

Abstract

The average daily gain (ADG) and body weight (BW) are very important traits for breeding programs and for the meat production industry, which have attracted many researchers to delineate the genetic architecture behind these traits. In the present study, single- and multi-trait genome-wide association studies (GWAS) were performed between imputed whole-genome sequence data and the traits of the ADG and BW at different stages in a large-scale White Duroc × Erhualian F population. A bioinformatics annotation analysis was used to assist in the identification of candidate genes that are associated with these traits. Five and seven genome-wide significant quantitative trait loci (QTLs) were identified by single- and multi-trait GWAS, respectively. Furthermore, more than 40 genome-wide suggestive loci were detected. On the basis of the whole-genome sequence association study and the bioinformatics analysis, NDUFAF6, TNS1 and HMGA1 stood out as the strongest candidate genes. The presented single- and multi-trait GWAS analysis using imputed whole-genome sequence data identified several novel QTLs for pig growth-related traits. Integrating the GWAS with bioinformatics analysis can facilitate the more accurate identification of candidate genes. Higher imputation accuracy, time-saving algorithms, improved models and comprehensive databases will accelerate the identification of causal genes or mutations, which will contribute to genomic selection and pig breeding in the future.

摘要

平均日增重(ADG)和体重(BW)是育种计划和肉类生产行业非常重要的性状,吸引了许多研究人员去描绘这些性状背后的遗传结构。在本研究中,在一个大规模的大白杜洛克×二花脸F群体中,对推算的全基因组序列数据与不同阶段的ADG和BW性状进行了单性状和多性状全基因组关联研究(GWAS)。使用生物信息学注释分析来辅助鉴定与这些性状相关的候选基因。单性状和多性状GWAS分别鉴定出5个和7个全基因组显著的数量性状位点(QTL)。此外,还检测到40多个全基因组暗示性位点。基于全基因组序列关联研究和生物信息学分析,NDUFAF6、TNS1和HMGA1作为最强的候选基因脱颖而出。使用推算的全基因组序列数据进行的单性状和多性状GWAS分析,鉴定出了几个与猪生长相关性状的新QTL。将GWAS与生物信息学分析相结合,可以促进更准确地鉴定候选基因。更高的推算准确性、省时的算法、改进的模型和综合数据库将加速因果基因或突变的鉴定,这将有助于未来的基因组选择和猪育种。

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