Van Steen Kristel
GIGA-R Medical Genomics – BIO3, University of Liège, Liège, Belgium; WELBIO, University of Liège, Liège, Belgium and Department of Human Genetics, University of Leuven, Leuven, Belgium
Systems genetics can be defined as the branch of systems biology referring to the integration of omics scale measurements, from genome to metabolome, and to functome representing biological functions, through transcriptome and proteome data.1 Any system has a defined structure and function and involves a set of interrelated components that work together towards a common goal, for instance maintaining or improving health in patient-centred health-care. It leaves no doubt that when viewing systems and their properties as wholes rather than collections of parts, extra information can be gained, on top of pragmatic solutions provided by the analysis of a system’s reduction into simpler components.
In this omics era, there is an increasing number of fields that suggest the potential benefits of explicitly accounting for or looking for relationships between informative components of complex systems. One of these fields that come naturally is epistasis, involving the analysis of gene-gene interactions. Notably, the epistasis field has grown into a more general theory and application framework for the analysis of interactions across and between –omics strata.2 In this larger framework, we show that there is an opportunity for flexibly manoeuvring between holistic and reductionist approaches. We illustrate this via an analytic example on gene-centric epistasis analysis using multiple omics, and an example taken from precision medicine, where the goal is to provide the most optimal treatment or clinical care for a small group of patient, taking into account individual variability in genes, environment, and lifestyle. One of the instruments for the latter includes systems genetics driven molecular profiling.
 Kadarmideen, H.N., von Rohr, P. & Janss, L.L. From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding. Mamm Genome 17, 548-564 (2006).
 Van Steen, K. & Malats, N. Perspectives on Data Integration in Human Complex Disease Analysis. in Big Data Analytics in Bioinformatics and Healthcare (ed. Baoying Wang (Waynesburg University, U., Ruowang Li (Pennsylvania State University, USA) and William Perrizo (North Dakota State University, USA)) (2015).