Animal Molecular Breeding 2024, Vol.14, No.2, 141-153 http://animalscipublisher.com/index.php/amb 146 6 Integrative Omics Approaches 6.1 Concept and importance of multi-omics integration The concept of multi-omics integration involves the comprehensive analysis of various omics data types, such as genomics, transcriptomics, proteomics, and metabolomics, to gain a holistic understanding of biological systems. This integrative approach is crucial for elucidating the complex interactions and regulatory mechanisms that underlie phenotypic traits. By combining data from different omics layers, researchers can uncover the intricate networks and pathways that drive biological processes, leading to more accurate predictions and better-informed breeding strategies (Suravajhala et al., 2016; Subramanian et al., 2020; Yang et al., 2021). Figure 2 Conceptual overview of multi-omics data integration in the context of biological research (Adopted from Jendoubi, 2021) Image caption: The figure is divided into several overlapping sections, each representing a key component of a multi-omics approach, showing how these components intersect to create a comprehensive research framework (Adopted from Jendoubi, 2021) 6.2 Strategies for integrating genomic, transcriptomic, proteomic, and metabolomic data Several strategies have been developed to integrate multi-omics data effectively. These include data-driven approaches, which rely on statistical and computational methods to identify correlations and interactions between different omics layers, and knowledge-based approaches, which use existing biological knowledge to guide the integration process. Simultaneous and step-wise integration methods are also employed to combine data from multiple omics layers in a coherent manner. Tools such as mixOmics for R software have been specifically designed to address data integration issues, enabling researchers to perform comprehensive analyses and derive meaningful insights from heterogeneous datasets (Duruflé et al., 2020; Subramanian et al., 2020; Wörheide et al., 2021). 6.3 Benefits and challenges of integrative omics in breeding The integration of multi-omics data offers numerous benefits for livestock breeding. It enhances the understanding of the genetic architecture underlying important economic traits, improves the accuracy of genomic predictions, and facilitates the identification of biomarkers for disease resistance and performance traits. However, several challenges remain, including the high dimensionality and heterogeneity of omics data, the need for robust bioinformatics tools, and the complexity of modeling interactions between different biological layers. Despite these challenges, initiatives such as the Functional Annotation of Animal Genomes (FAANG) project are making significant strides in addressing these issues and advancing the field of integrative omics (Figure 3) (Suravajhala et al., 2016; Diniz and Ward, 2021; Verardo et al., 2023).
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