Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

Abstract

In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.

Fig. 1

Overview of integrated genomics with various other ‘omics’ platforms/data types created via array-based or spectrometry or NGS technologies and systems genomics analyses. a Collection of multiple types of ‘omics’ datasets in farm or companion animals in controlled experimental conditions or in field experiments. b Systems genomics involves analysis of single-layer (vertical arrows) and multi-layer ‘omic’ datasets (horizontal arrow) ranging from GWAS, differential expression or methylation analyses through proteomic/metabolomic datasets to eQTL/mQTL/pQTL and network analyses. c Typical results of systems genomics involve single- and multi-layer analyses from GWAS Manhattan plots, genome-wide epistatic heat plots, variant detection or transcript counts in NGS data and gene expression heat plots through eQTL maps, gene regulatory or co-expression networks of eQTL or protein–protein interaction (PPI) networks to networks of differentially connected genes. The eSNP/eQTL box plot is taken from Kogelman et al. [115]. The remaining images in this panel are from the authors’ own unpublished material. d Potential applications of such approaches involve identification of causal genes or pathways, biomarkers, drug targets, various networks for a specific trait level or disease state and individualized genomic predictions of performance or disease risk