SNP阵列评价作为乌拉圭Corriedale羊遗传改良的工具

B. Carracelas, E. Navajas, B. Vera, G. Ciappesoni
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引用次数: 1

摘要

胃肠道线虫(GIN)的一种控制策略是遗传选择。本研究的目的是比较Corriedale品种的每克粪便卵数(FEC)和纤维直径(FD)估计育种值(EBV)和基因组EBV (GEBV)。分析包括19547只有数据的羔羊,分别从170、507和50K SNP芯片中获得454,711和383个基因型。采用单变量动物模型估计EBV和GEBV,其中当代组、出生类型和坝龄为固定效应,记录年龄为协变量。在基因组关系矩阵(G)中考虑差分权值(α),并使用赤池信息准则(AIC)识别最佳拟合模型,随后将其用于GEBV和精度估计。α的使用仅对低密度SNP芯片有影响。在整个人口的平均准确度上没有观察到差异。然而,在基因分型亚组中,使用170 SNP芯片的准确率提高了2% (α=0.25),使用507 SNP芯片的准确率分别提高了5% (α=0.5)和14% (α=0.75)。FD - EBV和GEBV的平均准确度无差异。这些结果表明,即使使用低密度芯片,也有可能提高GEBV精度。
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SNP arrays evaluation as tools in genetic improvement in Corriedale sheep in Uruguay
One control strategy for gastrointestinal nematodes (GIN) is genetic selection. This study´s objective was to compare eggs per gram of feces (FEC) and fiber diameter (FD) estimated breeding values (EBV) and genomic EBV (GEBV) in Corriedale breed. Analysis included 19547 lambs with data, and 454, 711 and 383 genotypes from 170, 507 and 50K SNP chips, respectively. A univariate animal model was used for EBV and GEBV estimation, which included contemporary group, type of birth and dam age as fixed effects, and age at recording as covariate. Differential weights (α) were considered in the genomic relationship matrix (G), and the best fit models were identified using Akaike´s Information Criterion (AIC), which were later used for GEBV and accuracies estimation. The use of α only impacted on low density SNP chips. No differences were observed in mean accuracies for the whole population. However, in the genotyped subgroup accuracies increased by 2% with the 170 SNP chip (α=0.25), and by 5% (α=0.5) and 14% (α=0.75) with the 507 SNP chip. No differences were observed in FD EBV and GEBV mean accuracies. These results show that it is possible to increase GEBV accuracies despite the use of low-density chips.
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