Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees and 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over 7 years, of which 154,318 from the last 4 years were genotyped. Training populations were constructed adding 1 year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first 3 years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of 1 year of genotypes in addition to 4 years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included 5 or 2 years of data, and a decrease of ~7% was observed when the training population included only 1 year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared with training sets without genomic data. The two most recent years of pedigree, phenotypic, and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.

中文翻译：

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研究肉鸡基因组预测准确性随时间推移的持久性

基因组预测的准确性是选择反应的重要组成部分。本研究的目的是：1) 调查肉鸡群的累积表型、基因型和谱系随着时间的推移预测准确度的趋势，以及 2) 测试来自远代的数据是否有助于维持选择候选者的预测准确度。数据包含 820K 表型的生长性状 (GT)、200K 的两种饲料效率性状（FE1 和 FE2）和 42K 的胴体产量性状 (CY)。谱系包括 7 年孵化的 1,252,619 只鸟，其中过去 4 年的 154,318 只进行了基因分型。构建训练人群，依次添加 1 年的数据，使用在训练种群之后的三代或之后的几年中孵化的鸟类的预测来评估准确性随时间的持久性。在第一代中，在基因型可用于训练人群（前 3 年的数据）之前，随着表型和谱系连续添加到累积的数据集中，准确性几乎保持稳定。在训练人群中加入 1 年的基因型以及 4 年的表型和谱系导致 GT 的准确度提高了 54%，FE1 提高了 76%，CY 提高了 110%，FE2 提高了 38%；平均而言，74% 的增长归因于基因组学。没有训练人群中的基因组信息，预测准确度下降得更快。当基因型不可用时，从第一代验证到第二代验证，跨性状预测准确度平均下降 41%，从第二代验证到第三代验证平均下降 51%。当基因型可用时，从第一代验证到第二代验证，性状平均下降 14%，从第二代验证到第三代验证平均下降 3%。当训练人群包含 5 年或 2 年的数据时，过去 3 代的预测准确率相同，而当训练人群仅包含 1 年的数据时，预测准确率下降了约 7%。与没有基因组数据的训练集相比，包括基因组信息的训练集提高了基因组预测的准确性和持久性。最近两年的谱系、表型、和基因组数据足以维持选择候选者的预测准确性。每年使用验证人群也获得了类似的结论。