Reliable single-nucleotide polymorphisms (SNP) effects from genomic best linear unbiased prediction BLUP (GBLUP) and single-step GBLUP (ssGBLUP) are needed to calculate indirect predictions (IP) for young genotyped animals and animals not included in official evaluations. Obtaining reliable SNP effects and IP requires a minimum number of animals and when a large number of genotyped animals are available, the algorithm for proven and young (APY) may be needed. Thus, the objectives of this study were to evaluate IP with an increasingly larger number of genotyped animals and to determine the minimum number of animals needed to compute reliable SNP effects and IP. Genotypes and phenotypes for birth weight, weaning weight, and postweaning gain were provided by the American Angus Association. The number of animals with phenotypes was more than 3.8 million. Genotyped animals were assigned to three cumulative year-classes: born until 2013 (N = 114,937), born until 2014 (N = 183,847), and born until 2015 (N = 280,506). A three-trait model was fitted using the APY algorithm with 19,021 core animals under two scenarios: 1) core 2013 (random sample of animals born until 2013) used for all year-classes and 2) core 2014 (random sample of animals born until 2014) used for year-class 2014 and core 2015 (random sample of animals born until 2015) used for year-class 2015. GBLUP used phenotypes from genotyped animals only, whereas ssGBLUP used all available phenotypes. SNP effects were predicted using genomic estimated breeding values (GEBV) from either all genotyped animals or only core animals. The correlations between GEBV from GBLUP and IP obtained using SNP effects from core 2013 were ≥0.99 for animals born in 2013 but as low as 0.07 for animals born in 2014 and 2015. Conversely, the correlations between GEBV from ssGBLUP and IP were ≥0.99 for animals born in all years. IP predictive abilities computed with GEBV from ssGBLUP and SNP predictions based on only core animals were as high as those based on all genotyped animals. The correlations between GEBV and IP from ssGBLUP were ≥0.76, ≥0.90, and ≥0.98 when SNP effects were computed using 2k, 5k, and 15k core animals. Suitable IP based on GEBV from GBLUP can be obtained when SNP predictions are based on an appropriate number of core animals, but a considerable decline in IP accuracy can occur in subsequent years. Conversely, IP from ssGBLUP based on large numbers of phenotypes from non-genotyped animals have persistent accuracy over time.

中文翻译：

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使用经过验证的和年轻的算法，可以对大量基因型动物进行间接预测。

需要基因组最佳线性无偏预测BLUP（GBLUP）和单步GBLUP（ssGBLUP）产生的可靠单核苷酸多态性（SNP）效应，才能计算出年轻基因型动物和未纳入官方评估的动物的间接预测（IP）。要获得可靠的SNP效应和IP，需要的动物数量最少，并且当有大量基因型动物可用时，用于成熟和幼龄的算法（APY）。因此，本研究的目的是评估数量越来越多的基因型动物的IP，并确定计算可靠的SNP效应和IP所需的最小动物数量。出生体重，断奶体重和断奶后体重的基因型和表型由美国安格斯协会提供。具有表型的动物数量超过380万。将基因型动物分为三个累积年级：出生至2013年（N = 114,937），出生至2014年（N = 183,847）和出生至2015年（N= 280,506）。在两种情况下，使用APY算法对19,021只核心动物进行了三特征模型拟合：1）所有年份的2013年核心（2013年之前出生的动物的随机样本）和2）2014年核心（直到2013年出生的动物的随机样本） 2014年）用于2014年类，2015年核心（2015年之前出生的动物的随机样本）用于2015年类。GBLUP仅使用基因型动物的表型，而ssGBLUP使用所有可用的表型。使用基因组估计育种值（GEBV）预测SNP效应）来自所有基因型动物或仅核心动物。使用2013年核心的SNP效应获得的GBLUP的GEBV与IP的相关性≥0.99，而对于2014年和2015年出生的动物则低至0.07。相反，ssGBLUP的GEBV与IP的相关性≥0.99。常年出生的动物。仅基于核心动物，通过ssGBLUP和SNP预测由GEBV计算的IP预测能力与基于所有基因型动物的IP预测能力一样高。使用2k，5k和15k核心动物计算SNP效应时，ssGBLUP的GEBV与IP之间的相关性分别为≥0.76，≥0.90和≥0.98。当SNP预测基于适当数量的核心动物时，可以获得基于GBLUP的基于GEBV的合适IP，但随后几年IP准确性可能会大大下降。反过来，