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基于全基因组关联研究的荷斯坦奶牛产奶量、乳脂肪和乳蛋白新基因座鉴定

GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle.

作者信息

Liu Liyuan, Zhou Jinghang, Chen Chunpeng James, Zhang Juan, Wen Wan, Tian Jia, Zhang Zhiwu, Gu Yaling

机构信息

School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China.

Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA.

出版信息

Animals (Basel). 2020 Nov 5;10(11):2048. doi: 10.3390/ani10112048.

Abstract

High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold ( < 4.0 × 10), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.

摘要

高产和优质牛奶是奶牛生产的主要目标。了解这些与牛奶相关性状的遗传结构是有益的,这样遗传变异就可以用于遗传改良。在本研究中,我们测量了中国荷斯坦牛群体的五个产奶量和品质性状。这些性状包括产奶量、脂肪和蛋白质。我们使用估计育种值作为因变量进行全基因组关联研究(GWAS)。通过使用线性混合模型,通过系谱关系估计育种值。使用Illumina BovineSNP150 BeadChip对具有表型的个体进行基因分型。使用固定和随机模型循环概率统一法(FarmCPU)进行关联分析。在全基因组显著阈值(<4.0×10)以上共检测到10个单核苷酸多态性(SNP),其中6个位于先前报道的数量性状位点(QTL)区域。我们在与相关SNP上下游120 kb距离内发现了8个候选基因。该研究不仅确定了基因对乳脂肪和蛋白质的影响,还发现了与牛奶性状相关的新遗传位点和候选基因。这些新的遗传位点将是奶牛分子育种的重要基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b90/7694478/ad2c93538a54/animals-10-02048-g001.jpg

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