Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to ‘phenomics’ instead of ‘genomics’. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and ‘missingness’ of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.

中文翻译：

---

现象：动物育种的当前前沿

基因组技术的进步超过了最乐观的预测，从而允许在工业规模上应用基因组选择。然而，除非确定引起突变，否则现在只能通过增加标记密度直至序列来预期遗传预测准确性上的边际提高。我们认为，一些最具有科学破坏性且与行业相关的挑战与“基因组学”有关，而不是与“基因组学”有关。由于传感器技术和人工智能的发展，已经有各种各样的分析工具可供使用，并且还将开发更多的分析工具。现在，我们可以解决行业中一些紧迫的社会需求，例如动物福利问题或资源利用效率。从统计和计算的角度来看，形态学提出了两个需要进一步工作的重要问题：惩罚和减少维度。数据固有的异质性和“缺失”将使情况变得复杂。总体而言，我们可以预期，精确的畜牧技术将使从大量动物中连续收集数百个性状成为可能。也许主要的革命将来自重新设计动物育种方案以明确允许使用高维表象学。同时，表型学数据肯定会启发我们基于表型的生物学基础上的知识。我们可以预期，精确的畜牧技术将使从大量动物中连续收集数百个特征成为可能。也许主要的革命将来自重新设计动物育种方案以明确允许使用高维表象学。同时，表型学数据肯定会启发我们基于表型的生物学基础上的知识。我们可以预期，精确的畜牧技术将使从大量动物中连续收集数百个特征成为可能。也许主要的革命将来自重新设计动物育种方案以明确允许使用高维表象学。同时，表型学数据肯定会启发我们基于表型的生物学基础上的知识。