Integration of 'omics' data in aging research: from biomarkers to systems biology

Abstract

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

Keywords: data integration; graphical models; high-throughput data; omics; systems biology.

Figure 1

Interdependencies of omics data: The figure illustrates dependencies which can be observed within almost any omics data set. Solid lines indicate biological processes which cause dependencies, while dashed lines represent observed associations.

Figure 2

Topological Properties of Biological Networks (A) is an excerpt from the human disease network (Goh et al., 2007). Nodes represent diseases; these are connected if they are associated with the same gene. Parkinson's disease connects three isolated disease clusters (colours), thus having a low clustering coefficient (0%) and high betweenness (72%). (B) is the close neighbourhood of the ApoD protein in a PPI network from STRING DB (Franceschini et al., 2013) using only experimentally confirmed interactions. ApoD connects two clusters and is, despite the low degree (2) and clustering coefficient (0%), a central node (betweenness centrality: 53%). In contrast, LEPR is central within the blue cluster (degree: 7, clustering: 14%).