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首页> 外文期刊>Livestock Science >Developing marker-assisted models for evaluating growth traits in Canadian beef cattle genetic improvement
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Developing marker-assisted models for evaluating growth traits in Canadian beef cattle genetic improvement

机译:建立用于评估加拿大肉牛遗传改良生长性状的标记辅助模型

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The Igenity (R) genotyping panel with a total of 233 SNP markers was used to genotype 2749 animals of the Beefbooster breeding stock population. A total of 144 SNP makers were used in conducting the association of the SNP markers and the growth traits based on multiple markers regression using stepwise method. The numbers of SNP markers that significantly (p<0.05) associated with birth weight (BWT), direct genetic effect of weaning weight (WWT), maternal genetic effect of weaning weight (Milk), yearling weight (YWT), mature weight (MWT), and scrotal circumference (SC) were 139, 135, 12, 89, 129 and 105, respectively. Marker score of each individual was calculated as the linear regression on the number of copies of specific allele of all significant (p<0.05) SNP markers. Bi-variable analysis of marker scores and phenotypes for all traits were conducted using ASReml Software and the genetic parameters for each trait were estimated. The genetic correlations between phenotype and marker score for BWT,WWT, Milk, YWT, MWT and SC were 0.61 +/- 0.08, 0.39 +/- 0.10, 0.14 +/- 0.03, 0.38 +/- 0.04, 0.57 +/- 0.09 and 0.54 +/- 0.10, respectively. The developed two-trait marker-assisted evaluation (TMAE) model increased the estimation accuracy of the phenotypic EBV of the animal when the genetic correlations between phenotype and marker score were high, even with the current limited genotypic and phenotypic information. The average prediction accuracy of phenotypic EBV for BWT, WWT, Milk, YWT, MWT and SC using TMAE were increased by 0.07, 0.07, 0.001, 0.42, 0.08 and 0.22, respectively
机译:具有总共233个SNP标记的Igenity(R)基因分型小组用于对Beefbooster育种种群的2749只动物进行基因分型。总共144个SNP制造商被用于进行SNP标记和生长性状的关联,这些标记基于逐步逐步回归的多个标记。与出生体重(BWT),断奶体重(WWT)的直接遗传效应,断奶体重(Milk)的母体遗传效应,一岁体重(YWT),成年体重(MWT)显着相关(p <0.05)的SNP标记数量)和阴囊周长(SC)分别为139、135、12、89、129和105。计算每个个体的标记得分,作为所有显着(p <0.05)SNP标记的特定等位基因拷贝数的线性回归。使用ASReml软件对所有性状的标记得分和表型进行双变量分析,并估算每种性状的遗传参数。 BWT,WWT,牛奶,YWT,MWT和SC的表型与标记得分之间的遗传相关性分别为0.61 +/- 0.08、0.39 +/- 0.10、0.14 +/- 0.03、0.38 +/- 0.04、0.57 +/- 0.09和0.54 +/- 0.10分别。当表型和标志物得分之间的遗传相关性很高时,即使当前的基因型和表型信息有限,发达的两性标志物辅助评估(TMAE)模型也可以提高动物表型EBV的估计准确性。使用TMAE对BWT,WWT,牛奶,YWT,MWT和SC的表型EBV的平均预测准确性分别提高了0.07、0.07、0.001、0.42、0.08和0.22

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