Different production systems and climates could lead to genotype-by-environment (G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions. In total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel. Single- and multi-trait models with genomic best linear unbiased prediction (GBLUP) and BayesC π were implemented to investigate their genomic prediction abilities with 20 replicates of five-fold cross-validation. Our results regarding between-environment genetic correlations of growth and reproductive traits (ranging from 0.618 to 0.723) indicated the existence of G × E interactions between these two Yorkshire pig populations. For single-trait models, genomic prediction with GBLUP was only 1.1% more accurate on average in the combined population than in single populations, and no significant improvements were obtained by BayesC π for most traits. In addition, single-trait models with either GBLUP or BayesC π produced greater bias for the combined population than for single populations. However, multi-trait models with GBLUP and BayesC π better accommodated G × E interactions, yielding 2.2% – 3.8% and 1.0% – 2.5% higher prediction accuracies for growth and reproductive traits, respectively, compared to those for single-trait models of single populations and the combined population. The multi-trait models also yielded lower bias and larger gains in the case of a small reference population. The smaller improvement in prediction accuracy and larger bias obtained by the single-trait models in the combined population was mainly due to the low consistency of linkage disequilibrium between the two populations, which also caused the BayesC π method to always produce the largest standard error in marker effect estimation for the combined population. In conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions.

中文翻译：

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在有限数量的环境中具有基因型与环境相互作用的多性状模型在猪基因组预测中的优势

不同的生产系统和气候可能导致种群之间的基因型-环境（G×E）相互作用，并且包含G×E相互作用在育种决策中变得至关重要。本研究的目的是研究多性状模型在有限数量的具有 G × E 相互作用的环境中在基因组预测中的性能。使用 PorcineSNP80 面板对来自两个具有相似遗传背景的约克郡猪群的 2,688 和 1,384 名具有生长和繁殖表型的个体进行了基因分型。实施了具有基因组最佳线性无偏预测 (GBLUP) 和 BayesC π 的单性状和多性状模型，以通过 20 个重复的五折交叉验证来研究它们的基因组预测能力。我们关于生长和繁殖性状的环境间遗传相关性（范围从 0.618 到 0.723）的结果表明这两个约克夏猪种群之间存在 G × E 相互作用。对于单性状模型，GBLUP 的基因组预测在组合群体中的平均准确率仅比单群体中高 1.1%，而 BayesC π 对大多数性状没有显着改善。此外，具有 GBLUP 或 BayesC π 的单性状模型对组合群体产生的偏差大于对单一群体的偏差。然而，具有 GBLUP 和 BayesC π 的多性状模型更好地适应了 G × E 相互作用，对生长和生殖性状的预测准确度分别提高了 2.2% – 3.8% 和 1.0% – 2.5%，与单种群和组合种群的单性状模型相比。在参考人群较小的情况下，多特征模型也产生了较低的偏差和较大的收益。单性状模型在组合群体中得到的预测准确率提高较小，偏差较大，主要是由于两个群体之间连锁不平衡的一致性较低，这也导致贝叶斯C π 方法总是产生最大的标准误差。组合人群的标记效应估计。总之，我们的研究结果证实，在存在 G × E 相互作用的情况下，直接组合种群以扩大参考种群对于提高基因组预测的准确性并不有效，