Diving through the "-omics": the case for deep phenotyping and systems epidemiology

Abstract

Enabled by diverse high-throughput technologies, the rapidly evolving field of "-omics sciences" offers the potential to study health and disease in breadth and depth at the human population level. We have recently linked genomics and metabolomics to present the first genome-wide association study of metabolic traits in human urine providing new insights into the functional background of chronic kidney disease. We propose systems epidemiology as a novel approach to study the complexities of human pathophysiology by integrating various population-level omic-metrics and to identify new trans-omic biomarkers.

FIG 1.

Systems epidemiology versus the classic single-level paradigm to study health and disease at the human population level. Integrating various population-level omic-metrics including the Phenome (physical traits such as body height, weight, or specific personality characteristics), Metabolome (complete set of small-molecule metabolites to be found within a biological sample), Proteome (entire set of proteins expressed by a genome, cell, tissue, or organism), Transcriptome (information about the expression of individual genes at the messenger ribonucleic acid level), Genome (complete set of genes in the human organism), and environmental factors (behavioral, sociodemographic, and group levels), as well as the complexities of its interactions will be critical for developing the most effective diagnostic techniques in systems epidemiology. The understanding of each system-level component is also crucial in understanding the pathophysiology of human disease (gray squares), here shown as a function of subnetworks of a complex multiomics network (each coloured node in the subnetwork represents an -omic level, whereas node sizes are proportional to the strength of disease association and links between nodes indicate trans-omic relationships). The systems epidemiology approach is contrasted by the simplicity of the single-level paradigm in classical epidemiology focusing mostly on a single risk factor or omic-level related to a disease, respectively.