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 programmes, 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, in many cases, sample collection becomes a limiting factor in experimental design as domestic animal populations can be stratified across the globe and data collection can involve animal sacrifice; therefore, specialized, integrative approaches are necessary to fully leverage existing data sets.
It is an exciting time for animal bioscience. The ability to apply high-throughput technologies to generate very large data sets in animals is a major leap forward for many species. This Frontiers Research Topic will focus on submissions that describe research work using these data-rich technologies specifically in livestock and other domestic animals. We encourage contributions that apply innovative methods and report novel results such as:
- Integrating different types of genomic and other high-throughput biological data to generate new scientific knowledge concerning genomic regulation of complex production, health, welfare and behavioural traits.
- Network biology approaches for analysis, visualisation and interpretation of multi-layered integrative omics data sets.
- Development of standardised computational pipelines and software that are particularly suited for investigating biological characteristics of domestic animals and livestock.
- 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.
This Research Topic is highly relevant to the
Functional Annotation of Animal Genomes(FAANG) Initiative, particularly the goals of the
FAANG Integrative Genomics and Network Biology (FNB) Group and we aim to encourage publication of outputs from FAANG-related research projects.