Animal Molecular Breeding 2024, Vol.14, No.2, 141-153 http://animalscipublisher.com/index.php/amb 147 Figure 3 Overview of integrated genomics with various other ‘omics’ platforms/data types created via array-based or spectrometry or NGS technologies and systems genomics analyses (Adopted from Suravajhala et al., 2016) Image caption: This figure is an overview of integrated genomics with various ‘omics’ platforms/data types and systems genomics analyses, illustrating how different types of biological data can be collected, analyzed, validated, and applied in animal health, production, and welfare (Adopted from Suravajhala et al., 2016) 6.4 Case study: multi-omics approach to improve dairy production A notable example of the application of multi-omics integration in livestock breeding is the improvement of dairy production. By combining genomic, transcriptomic, proteomic, and metabolomic data, researchers have been able to identify key regulatory mechanisms and genetic variants associated with milk yield and quality. For instance, multi-tissue transcriptomic profiling has revealed differential tissue regulation mechanisms in nutrient-restricted bovine fetuses, providing insights into the genetic basis of milk production traits. These findings have the potential to inform breeding programs aimed at enhancing dairy production efficiency and sustainability (Diniz and Ward, 2021; Pazhamala et al., 2021; Verardo et al., 2023). 7 Case Study: Integrative Omics Application in Livestock Breeding 7.1 Background of the selected case study The selected case study focuses on the application of integrative omics approaches in cattle breeding. Traditional quantitative genetics has significantly advanced the selection of cattle for specific traits, yet considerable phenotypic variation remains unexplained. This gap represents an opportunity for further improvement in animal production. The advent of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has revolutionized the understanding of the genetic architecture controlling traits of interest. These technologies offer a systems biology approach to animal breeding, providing a comprehensive understanding of the biological mechanisms underlying phenotypic traits (Berry et al., 2011; Diniz and Ward, 2021). 7.2 Omics data integration process The integration of omics data involves combining information from various biological layers to create a holistic view of the genetic and phenotypic landscape. In cattle breeding, this process typically starts with the collection of
RkJQdWJsaXNoZXIy MjQ4ODYzNA==