Hidalgo Jorge, Lourenco Daniela, Tsuruta Shogo, Masuda Yutaka, Miller Stephen, Bermann Matias, Garcia Andre L S, Misztal Ignacy
Department of Animal and Dairy Science, University of Georgia, Athens, GA.
Angus Genetics Inc., St. Joseph, MO.
J Anim Sci. 2021 Feb 1;99(2). doi: 10.1093/jas/skab004.
The stability of genomic evaluations depends on the amount of data and population parameters. When the dataset is large enough to estimate the value of nearly all independent chromosome segments (~10K in American Angus cattle), the accuracy and persistency of breeding values will be high. The objective of this study was to investigate changes in estimated breeding values (EBV) and genomic EBV (GEBV) across monthly evaluations for 1 yr in a large genotyped population of beef cattle. The American Angus data used included 8.2 million records for birth weight, 8.9 for weaning weight, and 4.4 for postweaning gain. A total of 10.1 million animals born until December 2017 had pedigree information, and 484,074 were genotyped. A truncated dataset included animals born until December 2016. To mimic a scenario with monthly evaluations, 2017 data were added 1 mo at a time to estimate EBV using best linear unbiased prediction (BLUP) and GEBV using single-step genomic BLUP with the algorithm for proven and young (APY) with core group fixed for 1 yr or updated monthly. Predictions from monthly evaluations in 2017 were contrasted with the predictions of the evaluation in December 2016 or the previous month for all genotyped animals born until December 2016 with or without their own phenotypes or progeny phenotypes. Changes in EBV and GEBV were similar across traits, and only results for weaning weight are presented. Correlations between evaluations from December 2016 and the 12 consecutive evaluations were ≥0.97 for EBV and ≥0.99 for GEBV. Average absolute changes for EBV were about two times smaller than for GEBV, except for animals with new progeny phenotypes (≤0.12 and ≤0.11 additive genetic SD [SDa] for EBV and GEBV). The maximum absolute changes for EBV (≤2.95 SDa) were greater than for GEBV (≤1.59 SDa). The average(maximum) absolute GEBV changes for young animals from December 2016 to January and December 2017 ranged from 0.05(0.25) to 0.10(0.53) SDa. Corresponding ranges for animals with new progeny phenotypes were from 0.05(0.88) to 0.11(1.59) SDa for GEBV changes. The average absolute change in EBV(GEBV) from December 2016 to December 2017 for sires with ≤50 progeny phenotypes was 0.26(0.14) and for sires with >50 progeny phenotypes was 0.25(0.16) SDa. Updating the core group in APY without adding data created an average absolute change of 0.07 SDa in GEBV. Genomic evaluations in large genotyped populations are as stable and persistent as the traditional genetic evaluations, with less extreme changes.
基因组评估的稳定性取决于数据量和群体参数。当数据集足够大以估计几乎所有独立染色体片段的值时(美国安格斯牛约为10K),育种值的准确性和持续性将会很高。本研究的目的是调查在一个大型基因分型肉牛群体中,1年内每月评估的估计育种值(EBV)和基因组EBV(GEBV)的变化情况。所使用的美国安格斯牛数据包括820万条出生体重记录、890万条断奶体重记录和440万条断奶后增重记录。截至2017年12月出生的共有1010万头动物有系谱信息,其中484,074头进行了基因分型。一个截断数据集包括截至2016年12月出生的动物。为模拟每月评估的情况,每次添加1个月的2017年数据,使用最佳线性无偏预测(BLUP)估计EBV,使用单步基因组BLUP和适用于成年和幼年动物的算法(APY)估计GEBV,核心群体固定1年或每月更新。将2017年每月评估的预测结果与2016年12月或前一个月对截至2016年12月出生的所有基因分型动物(无论有无自身表型或后代表型)的评估预测结果进行对比。EBV和GEBV的变化在各性状间相似,仅展示断奶体重的结果。2016年12月评估与连续12次评估之间的相关性,EBV≥0.97,GEBV≥0.99。EBV的平均绝对变化比GEBV小约两倍,除了有新后代表型的动物(EBV和GEBV的加性遗传标准差[SDa]分别≤0.12和≤0.11)。EBV的最大绝对变化(≤2.95 SDa)大于GEBV(≤1.59 SDa)。2016年12月至2017年1月和12月,幼年动物GEBV的平均(最大)绝对变化范围为0.05(0.25)至0.10(0.53)SDa。有新后代表型动物的GEBV变化相应范围为0.05(0.88)至0.11(1.59)SDa。2016年12月至2017年12月,后代表型≤50的公牛EBV(GEBV)的平均绝对变化为0.26(0.14)SDa,后代表型>50的公牛为0.25(0.16)SDa。在APY中更新核心群体而不添加数据,GEBV的平均绝对变化为0.07 SDa。大型基因分型群体中的基因组评估与传统遗传评估一样稳定和持久,变化较小。