Phenomes: the current frontier in animal breeding

Abstract

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.

Applications of Omics Technology for Livestock Selection and Improvement

“…However, organism-wide phenotypic data of animals during different growth phases, various physiological or production stages, in response to dietary changes or upon their selective breeding (i.e., phenome-level data) are challenging to maintain and are generally missing (Pérez-Enciso and Steibel 2021). Accurate phenomics data on adaptability, fitness, body conformation, disease resistance/susceptibility, production performance, reproduction, and growth characteristics will help better estimate accurate BV and selection of genetically superior animals (Juárez et al, 2021;Pérez-Enciso and Steibel 2021). Particular emphasis should be given for multi-omics with other "big data"; for example, those detected by advanced management technologies (e.g., using remote sensors communicating with the Internet of Things to measure physiological and behavioral data, which can be applied to monitor estrus, lameness, or rumination) to have complete data set (Sun et al, 2019).…”

Progress and opportunities through use of genomics in animal production

“…To fully harness the potential benefits of PLF, several major challenges need to be overcome. For example, suitable data infrastructure (including strong broadband access in rural areas) will need to be developed that can support effective data sharing in real time, along with advanced analytical methods for the increasingly complex multidimensional data sets that will become available [21,[100][101][102]. Techniques, such as data mining, computational intelligence, machine learning, time series, and pattern recognition, are already being used to successfully analyse complex data sets from livestock systems and are likely to become increasingly important as more data are collected [103][104][105][106][107].…”

Trends towards revealing the genetic architecture of sheep tail patterning: Promising genes and investigatory pathways

“…By considering the “phenomics” concept, ‘the acquisition of high‐dimensional phenotypic data on an organism‐wide scale’ (Houle et␣al . 2010; Pérez‐Enciso & Steibel, 2021) could lead to “precision animal breeding”, which is ‘the advanced extension of classical breeding with genomic and molecular methods’ (Flint & Woolliams, 2008). Integrating classical breeding with modern phenotyping approaches, genomic and molecular genetic methods would help investigate the genetic architecture of significant production traits.…”