The Molecular Human – A Roadmap of Molecular Interactions Linking Multiomics Networks with Disease Endpoints

Abstract

In-depth multiomics phenotyping can provide a molecular understanding of complex physiological processes and their pathologies. Here, we report on the application of 18 diverse deep molecular phenotyping (omics-) technologies to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes study QMDiab. We integrated quantitative readouts of 6,304 molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts using between-platform mutual best correlations, within-platform partial correlations, and genome-, epigenome-, transcriptome-, and phenome-wide associations. The achieved molecular network covers over 34,000 statistically significant trait-trait links and illustrates ″The Molecular Human″. We describe the variances explained by each omics layer in the phenotypes age, sex, BMI, and diabetes state, platform complementarity, and the inherent correlation structures of multiomics. Finally, we discuss biological aspects of the networks relevant to the molecular basis of complex disorders. We developed a web-based interface to ″The Molecular Human″, which is freely accessible at http://comics.metabolomix.com and allows dynamic interaction with the data.

AutoFocus: A hierarchical framework to explore multi-omic disease associations spanning multiple scales of biomolecular interaction

“…For access to content described in this manuscript see: http://doi.org/10.7303/syn26401311. (2) The full complement of clinical and demographic data for the ROS/MAP cohort are available via the Rush AD Center Resource Sharing Hub and can be requested at https://www.radc.rush.edu.…”

“…The increasing availability of high-throughput measurement technologies has led to the generation of a large number of multi-omics datasets, providing molecular snapshots of biological systems at all -omic levels of regulation 1,2 . Such multi-omic datasets can be explored to infer molecular interactions [3][4][5] , or in the context of disease, to identify perturbations for a deeper understanding of pathophysiological mechanisms [6][7][8] .…”

“…The method is accompanied by an interactive application that allows a user to explore the hierarchy created by their data and provides functional insights through module annotation and the identification of module members driving phenotype association (Figure 1). We then apply our method to two independent datasets to validate its ability to capture known disease signal and explore new findings: Type 2 Diabetes in The Qatar Metabolomics Study on Diabetes (QMDiab, n = 388) 2 , which contains 12 multiomic datasets including metabolomics, proteomics, and glycomics; and the Alzheimer's Disease in the Religious Orders Study/Memory and Aging Project (ROS/MAP, n = 500) 31 , which includes a metabolomics and proteomics platform and multiple clinical phenotypes. Finally, we show how our method can easily integrate other hierarchical clustering methods into its analysis pipeline, such as the popular WGCNA clustering method 10 .…”