Animal Molecular Breeding 2024, Vol.14, No.2, 141-153 http://animalscipublisher.com/index.php/amb 141 Review Article Open Access Integrative Omics Approaches for Improving Livestock Breeding Strategies Haimei Wang Hainan Institute of Biotechnology, Haikou, 570206, Hainan, China Corresponding email: haimei.wang@hibio.org Animal Molecular Breeding, 2024, Vol.14, No.2 doi: 10.5376/amb.2024.14.0016 Received: 14 Jan., 2024 Accepted: 26 Feb., 2024 Published: 07 Mar., 2024 Copyright © 2024 Wang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang H.M., 2024, Integrative omics approaches for improving livestock breeding strategies, Animal Molecular Breeding, 14(2): 141-153 (doi: 10.5376/amb.2024.14.0016) Abstract The livestock sector is under increasing pressure to meet the growing demand for animal products while improving animal health, performance, and reducing environmental impact. Integrative omics approaches, encompassing genomics, transcriptomics, proteomics, epigenomics, and metabolomics, offer promising avenues to address these challenges. These technologies enable a deeper understanding of the genetic and molecular mechanisms underlying economically important traits, such as feed efficiency, meat quality, and disease resistance. Despite significant advancements, the integration of multi-omics data into breeding strategies remains complex, requiring sophisticated analytical methods and better functional genome annotation. Initiatives like the Functional Annotation of Animal Genomes (FAANG) project are pivotal in overcoming these limitations. By leveraging integrative network modeling and multi-tissue transcriptomic profiles, researchers can elucidate the intricate regulatory networks that drive genotype-phenotype associations. This comprehensive approach is expected to enhance the accuracy of genomic predictions and breeding values, ultimately leading to more efficient and sustainable livestock production systems. Keywords Integrative omics; Livestock breeding; Genomic selection; Multi-omics data; Functional genome annotation 1 Introduction Livestock breeding has long been a cornerstone of agricultural practices, aimed at enhancing desirable traits such as productivity, health, and adaptability in farm animals. Traditional breeding methods have relied heavily on phenotypic selection and quantitative genetics to achieve genetic gains in traits like milk, meat, and egg production. However, these methods often fall short in addressing complex traits such as disease resistance, fertility, and behavior, which are influenced by multiple genetic and environmental factors (Camara et al., 2019; Baes et al., 2022). As global demand for animal products continues to rise, there is an increasing need for more efficient and sustainable breeding strategies. Modern breeding techniques, such as genomic selection and marker-assisted selection, have revolutionized the field by enabling more precise and accelerated genetic improvements. These methods have been particularly effective in enhancing traits that are difficult to measure or have low heritability (Mutenje et al., 2020). However, the full potential of these techniques is yet to be realized, especially in developing countries where traditional practices still dominate (Camara et al., 2019; Mutenje et al., 2020). The advent of omics technologies-genomics, transcriptomics, proteomics, epigenomics, and metabolomics-has opened new avenues for understanding the genetic architecture of complex traits in livestock. These technologies allow for a comprehensive analysis of the molecular mechanisms underlying phenotypic variations, thereby facilitating more informed breeding decisions (Diniz and Ward, 2021; Banerjee et al., 2022; Verardo et al., 2023). For instance, integrative network modeling and multi-omics approaches have been employed to untangle the biological mechanisms driving genotype-phenotype associations, thereby improving the accuracy of trait predictions (Diniz and Ward, 2021; Berry et al., 2011). Projects like the Functional Annotation of Animal Genomes (FAANG) have further enriched our understanding by providing extensive datasets on various livestock species (Verardo et al., 2023). This study aims to explore the integrative use of omics technologies to enhance livestock breeding strategies. By leveraging multi-omics data, we seek to develop more precise and sustainable breeding programs that can meet
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