Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation.
Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation.
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DOI：
10.1534/g3.114.010504
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发表时间：
2014-04-16
期刊：
影响因子：
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通讯作者：
Steibel JP
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文献类型：
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作者：
Badke YM;Bates RO;Ernst CW;Fix J;Steibel JP
基因组选择具有促进遗传进步的潜力。高密度单核苷酸多态(SNP)基因分型可提高猪育种基因组育种值(GEBV)预测的成本效益。因此，这项工作的目标是：(1)估计约克郡群体中三个性状的基因组评估和GEBV的准确性；(2)量化在两种情况下基因组评估和GEBV的准确性损失：高成本、高精度的情景，其中仅从低密度平台输入选择候选；以及低成本、低精度的情景，其中所有的动物都使用一个小的单倍型参考小组。用PorcineSNP60珠芯片获得了983头约克夏公猪的表型和基因型别。使用GeneSeek基因组简档(10K)中的tag SNP掩蔽和推算选择候选的基因类型。Beagle用128或1800个单倍型作为参照板进行归因。用去回归的繁殖值作为响应变量，通过以动物为中心的岭回归模型得到GEBV。在10倍交叉验证设计中，基因组评估的准确性被估计为估计的育种值和GEBV之间的相关性。用观察到的基因类型对所有性状进行基因组评估的准确性都很高(0.65−0.68)。使用从大型参考小组(精度：R2=0.95)输入的基因型进行基因组评估不会显著降低准确性，而使用从小型参考小组(R2=0.88)输入的基因型的情景确实显示准确性显著降低。基于候选选择的基因类型的基因组评估可以用使用观察到的基因类型进行基因组评估的成本的一小部分来实施，并且仍然产生几乎相同的准确性。另一方面，使用一个非常小的单倍型参考小组来归因于训练动物和候选动物进行选择会导致基因组评估的准确性降低。
Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes. Phenotypes and genotypes obtained with the PorcineSNP60 BeadChip were available for 983 Yorkshire boars. Genotypes of selection candidates were masked and imputed using tagSNP in the GeneSeek Genomic Profiler (10K). Imputation was performed with BEAGLE using 128 or 1800 haplotypes as reference panels. GEBV were obtained through an animal-centric ridge regression model using de-regressed breeding values as response variables. Accuracy of genomic evaluation was estimated as the correlation between estimated breeding values and GEBV in a 10-fold cross validation design. Accuracy of genomic evaluation using observed genotypes was high for all traits (0.65−0.68). Using genotypes imputed from a large reference panel (accuracy: R2 = 0.95) for genomic evaluation did not significantly decrease accuracy, whereas a scenario with genotypes imputed from a small reference panel (R2 = 0.88) did show a significant decrease in accuracy. Genomic evaluation based on imputed genotypes in selection candidates can be implemented at a fraction of the cost of a genomic evaluation using observed genotypes and still yield virtually the same accuracy. On the other side, using a very small reference panel of haplotypes to impute training animals and candidates for selection results in lower accuracy of genomic evaluation.