Department of Animal and Dairy Science, University of Georgia, Athens, GA.
Angus Genetics Inc. St. Joseph, MO.
J Anim Sci. 2020 Jun 1;98(6). doi: 10.1093/jas/skaa154.
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.
需要可靠的单核苷酸多态性 (SNP) 效应，以便从基因组最佳线性无偏预测 (GBLUP) 和单步 GBLUP (ssGBLUP) 计算年轻基因型动物和未包含在官方评估中的动物的间接预测 (IP)。获得可靠的 SNP 效应和 IP 需要最小数量的动物，当有大量基因型动物时，可能需要使用已证明和年轻动物 (APY) 算法。因此，本研究的目的是评估随着基因型动物数量的增加，IP 的表现，并确定计算可靠 SNP 效应和 IP 所需的最小动物数量。出生体重、断奶体重和断奶后增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 万头。基因型动物被分配到三个累积的年度类别：2013 年之前出生的（N=114937）、2014 年之前出生的（N=183847）和 2015 年之前出生的（N=280506）。使用 APY 算法拟合了一个三性状模型，核心动物有 19021 头，在两种情况下：1）核心 2013 年（2013 年之前出生的动物随机样本）用于所有年度类别；2）核心 2014 年（2014 年之前出生的动物随机样本）用于 2014 年年度类别和核心 2015 年（2015 年之前出生的动物随机样本）用于 2015 年年度类别。GBLUP 仅使用基因型动物的表型，而 ssGBLUP 使用所有可用的表型。使用来自全基因组估计育种值 (GEBV) 的 SNP 效应预测 SNP 效应，这些 GEBV 来自所有基因型动物或仅核心动物。使用核心 2013 年 SNP 效应获得的 GBLUP 和 IP 的 GEBV 之间的相关性对于 2013 年出生的动物而言，≥0.99，但对于 2014 年和 2015 年出生的动物，最低低至 0.07。相反，ssGBLUP 中 GEBV 和 IP 之间的相关性对于所有年份出生的动物均≥0.99。使用基于仅核心动物的 SNP 预测和 ssGBLUP 中 GEBV 计算的 IP 预测能力与基于所有基因型动物的预测能力一样高。当使用 2k、5k 和 15k 核心动物计算 SNP 效应时，基于 ssGBLUP 中 GEBV 和 IP 的相关性分别≥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 中 GEBV 的适当 IP，但随后几年的 IP 准确性可能会大幅下降。相反，基于大量非基因型动物表型的 ssGBLUP 中的 IP 具有持久的准确性。
