BACKGROUND The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment. METHODS A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only. RESULTS Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds. CONCLUSIONS The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

中文翻译：

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利用基因组信息来探索生物安全和生产环境中肉鸡体重的基因型与环境的相互作用。

背景技术通过使用基因组信息来提高预测的准确性已被充分证明。然而，当生物安全育种 (B) 环境和商业生产 (C) 环境之间存在基因型与环境的相互作用 (G × E) 时，使用基因组信息和方法进行遗传评估的好处就消失了。在本研究中，我们探讨了 (1) 第 5 周和第 6 周时 G × E 与肉鸡体重 (BW) 的相互作用，以及 (2) 在培育候选品种并进行测试时，使用基因组信息预测 BW 性状的好处。 B环境和近亲在C环境中进行测试。方法 使用基于谱系的最佳线性无偏预测 (BLUP) 多变量模型来估计方差分量并预测在 B 和 C 环境中测量的第 5 周和第 6 周 BW 性状的育种值 (EBV)。结合谱系和基因组信息的单步基因组 BLUP (ssGBLUP) 模型用于预测 EBV。交叉验证基于 EBV 的相关性、平均差和回归斜率统计数据，这些统计数据是根据完整数据集和简化数据集估计的。这些统计数据是 B 和 C 环境中测量的性状 EBV 预测的总体准确性、偏差和离散度的指标。验证动物是仅在 B 环境中进行基因分型和未进行基因分型的鸟类。结果 对于 BW 性状，发现了由于环境差异而导致的 G × E 相互作用的一些迹象，包括在环境 B 和 C 中测量的 BW 的显着重新排序、异质方差和不同的遗传力。在环境 B 和 C 中测量的 BW 性状之间的遗传相关性范围从 0.48 到 0.54。与仅使用谱系信息相比，结合谱系和基因组信息的使用提高了 EBV 群体的准确性，并减少了基因型鸟类 EBV 预测的偏差。对于非基因型鸟类，也观察到 EBV 准确性略有增加，但对于非基因型鸟类，EBV 预测的偏差增加。结论 对于在环境 B 和 C 中测量的肉鸡 BW 性状，G × E 交互作用很强。与仅使用谱系信息相比，结合谱系和基因组信息显着提高了 B 环境中基因分型鸡的 EBV 群体准确性。