High-throughput genomics technologies are currently driving significant change in how genetic improvement is implemented in domestic animal species. In the past decade, genome-enabled breeding programs, genome-wide association (GWA) studies and population genomics surveys have generated massive single-nucleotide polymorphism (SNP) data sets for many production, landrace and heritage livestock populations. In recent years, these array-generated SNP data have also been integrated with whole-genome sequence (WGS) data, a trend that is likely to accelerate over the coming years. In parallel to this, high-resolution transcriptomics (e.g. RNA-seq, microRNA-seq), epigenomics (e.g. whole-genome bisulphite sequencing, ChIP-seq) and proteomics and metabolomics (e.g. LC-MS, GC-MS) data sets are being assembled for a range of animal tissues. In many livestock and domestic species, these high-throughput data types have only recently become available, which gives rise to several unique and interesting computational, analytical and interpretative challenges.

Each of these data-rich technologies describe one of the many layers that exist between genotype and phenotype. Integrative genomics describes experiments that join two or more of these data sets together. One common method to organize and compare these heterogeneous data is in the form of a network, which focuses on the relationships that arise among individual network components. Integrative and network-based approaches have been used extensively in human and model organisms; however, methods and techniques are not always directly translatable to livestock and domestic animals. Unique characteristics of agricultural species can require special consideration in the application and interpretation of integrative and network-based approaches. These include: genotype by environment (G × E) interactions; low effective population size; significant breed and population structure; heterosis; and the effects of intensive artificial selection. Several technical issues also need to be considered, including reference genomes that vary in quality and utility, incorporating legacy data sets generated using different technology platforms and varying levels of nucleotide diversity among diverse populations.

Furthermore, because domestic animal populations are stratified globally and data gathering may involve animal sacrifice, sample collecting frequently becomes a limiting issue in experimental design; hence, specialized, integrative techniques are required to effectively exploit current data sets.

The aim of this Special Issue is to collate original research and review articles that focus on employing unique approaches and present novel findings focused on integrative genomics and system biology in domestic animal species.

Potential topics include but are not limited to the following:

• Integrating many forms of genomic and other high-throughput biological data in order to further scientific understanding of the genetic control of complex production, health, welfare, and behavioral characteristics
• Approaches from network biology for analyzing, visualizing, and interpreting multi-layered integrative omics data sets
• Application of integrative genomics and network biology to identify targets for genome editing using CRISPR/Cas9 and comparable technologies
• Application of high-throughput biology (omics) data in species that are “genome-enabled” by domestic animals and of ecological and evolutionary interest (e.g. wild equids, suids and canids)
• High-throughput biology (omics) research in domestic animals that directly informs human biology, health or well-being
• Creation of standardized computational pipelines and tools that are well-suited for studying the biological features of domestic animals
• Using integrative genomics and network biology to discover potential targets for genome editing using CRISPR/Cas9 and related technologies
• Application of high-throughput biology (omics) data to species whose genomes are "enabled" by domestic animals and are of ecological and evolutionary significance
• The use of artificial intelligence in assessing the domestic animal production
• Use of machine learning in precision livestock farming
• Multi-omic data integration in systems genomics
• Omics and computer applications in animal production