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全基因组关联研究揭示了荷斯坦奶牛数字皮炎的易感基因座。

Genome-Wide Association Studies Reveal Susceptibility Loci for Digital Dermatitis in Holstein Cattle.

作者信息

Lai Ellen, Danner Alexa L, Famula Thomas R, Oberbauer Anita M

机构信息

Department of Animal Science, University of California, Davis, CA 95616, USA.

出版信息

Animals (Basel). 2020 Oct 31;10(11):2009. doi: 10.3390/ani10112009.

Abstract

Digital dermatitis (DD) causes lameness in dairy cattle. To detect the quantitative trait loci (QTL) associated with DD, genome-wide association studies (GWAS) were performed using high-density single nucleotide polymorphism (SNP) genotypes and binary case/control, quantitative (average number of FW per hoof trimming record) and recurrent (cases with ≥2 DD episodes vs. controls) phenotypes from cows across four dairies (controls = 129 vs. FW = 85). Linear mixed model (LMM) and random forest (RF) approaches identified the top SNPs, which were used as predictors in Bayesian regression models to assess the SNP predictive value. The LMM and RF analyses identified QTL regions containing candidate genes on autosome (BTA) 2 for the binary and recurrent phenotypes and BTA7 and 20 for the quantitative phenotype that related to epidermal integrity, immune function, and wound healing. Although larger sample sizes are necessary to reaffirm these small effect loci amidst a strong environmental effect, the sample cohort used in this study was sufficient for estimating SNP effects with a high predictive value.

摘要

趾间皮炎(DD)会导致奶牛跛行。为了检测与DD相关的数量性状位点(QTL),利用高密度单核苷酸多态性(SNP)基因型以及来自四个奶牛场奶牛的二元病例/对照、定量(每次蹄部修剪记录的趾间皮炎平均数量)和复发(≥2次DD发作的病例与对照)表型进行了全基因组关联研究(GWAS)(对照 = 129头,趾间皮炎 = 85头)。线性混合模型(LMM)和随机森林(RF)方法确定了顶级SNP,这些SNP被用作贝叶斯回归模型中的预测因子,以评估SNP的预测价值。LMM和RF分析确定了QTL区域,其中常染色体(BTA)2上的候选基因与二元和复发表型相关,BTA7和20上的候选基因与定量表型相关,这些表型与表皮完整性、免疫功能和伤口愈合有关。尽管在强大的环境影响下需要更大的样本量来再次确认这些小效应位点,但本研究中使用的样本队列足以估计具有高预测价值的SNP效应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e84f/7693332/bb02dddbc6e8/animals-10-02009-g001.jpg

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