282 Multi-omics Approaches to Improve Animal Production

Abstract

Abstract The livestock sector faces an immense challenge to meet the increased demand for animal products. There is a need to increase animal performance, improve animal health and well-being, and reduce the environmental footprint. To meet these challenges, omics technologies have provided opportunities to understand animal biology and the genetic architecture underlying important economic traits. Despite the increased number of quantitative trait loci identified, the reported number of causal genes and mutations driving phenotypic changes is still limited. Likewise, transcriptomics has been routinely explored to identify genes underlying complex traits such as feed efficiency and meat quality. Furthermore, other omics approaches, such as epigenomics, proteomics, and metabolomics, are now extensively used. However, most of the studies have focused on single-data-type designs without considering the intricated relationship among these regulatory layers. Additionally, it has been shown that the relationship between the host genome and its gut microbiota plays a pivotal role in shaping animal performance. Despite the improved genetic gain provided by genomic selection approaches, the genetic basis of complex traits remains unclear. As the cross-talk between regulatory layers modulates complex traits, integrative network modeling has been adopted to untangle the biological mechanisms driving genotype-phenotype association. For example, by combining multi-tissue transcriptomic profiles, we were able to identify putative mechanisms driving the differential tissue regulation in nutrient restricted bovine fetuses. Despite the advances in network modeling, the genome to phenome connection still requires a better functionally annotated genome. Furthermore, analytical methods to fully integrate omics data into breeding equations are still under development. However, initiatives, such as FAANG, seek to address some of the aforementioned limitations. A better understanding of the mechanisms underlying complex traits and the development of statistical models that includes omics data will increase the prediction accuracy of important economic traits. Altogether, it will allow improving production efficiency to meet society’s demand.