Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage. By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits. Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

中文翻译：

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将微生物组信息纳入多性状基因组评估：肉牛纵向生长性能的案例研究


生长率是牛饲料转化效率的重要组成部分，并且在肥育期的不同阶段有所不同。瘤胃微生物群的代谢效应对于牛的生长至关重要，研究造成这种时间变化的基因组和微生物因素有助于最大限度地提高每个生长阶段的饲料转化效率。通过分析育肥期的纵向体重以及 359 头肉牛的基因组和宏基因组数据，我们的研究表明，宿主基因组对功能性瘤胃微生物组的影响导致了不同月份平均日增重 (ADG) 的时间变化。 ADG1、ADG2、ADG3、ADG4)。 533 个加性对数比转化微生物基因 (alr-MG) 与至少一种 ADG 性状（范围从 |0.21| 到 |0.42|）具有非零基因组相关性 (rg)。只有少数 ar-MG 与不止一种 ADG 性状相关，这表明不同阶段 ADG 的基础是不同的宿主微生物组决定论。这些alr-MG参与核糖体生物合成、能量过程、硫和氨基酸代谢和运输，或脂多糖信号传导等。我们选择了 32 个 alr-MG 的两个替代子集，它们对所有 ADG 性状具有不均匀或均匀的 rg 符号，无论 rg 大小如何，并使用它们开发基于 alr-MG 的微生物组驱动的育种策略单独使用，或与 ADG 性状相结合，旨在塑造瘤胃微生物组，以提高所有育肥阶段的 ADG。 相对于直接育种策略（仅使用 ADG 性状），将 alr-MG 信息与 ADG 记录相结合可将基因组估计育种值 (GEBV) 的预测准确性提高 11% 至 22%，而仅使用微生物组信息则获得较低的准确性（从7 至 41%）。预测的选择反应随准确度的变化而变化。与非均匀子集相比，基于 rg 符号（均匀子集）限制 ar-MG 并没有在预测响应中产生增益，这是因为缺少 alr-MG 至少显示非零 rg 超过ADG 特征之一。我们的工作揭示了微生物代谢在基因组水平上肉牛生长轨迹中的作用，并深入了解在未来基因组育种计划中使用微生物组信息的潜在好处，以准确估计 GEBV 并增加每个育种阶段的 ADG肉牛。