A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes
Anna Halama, Shaza B. Zaghlool, Gaurav Thareja, Sara Abdul Kader, Wadha Al‐Muftah, Marjonneke J. Mook-Kanamori, Hina Sarwath, Yasmin A. Mohamoud, Nisha Stephan, Sabine Ameling, Maja Pučić‐Baković, Jan Krumsiek, Cornelia Prehn, Jerzy Adamski, Jochen M. Schwenk, Nele Friedrich, Uwe Völker, Manfred Wuhrer, Gordan Lauc, S. Hani Najafi‐Shoushtari, Joel A. Malek, Johannes Graumann, Dennis O. Mook‐Kanamori, Frank Schmidt, Karsten Suhre
Abstract
In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays “The Molecular Human”. We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to “The Molecular Human” ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities. Multiomic phenotyping provides molecular insights into complex physiological processes and pathologies. The study uses 18 omics platforms to analyze biofluids from 391 participants. It constructs a comprehensive molecular network based on omics integration, revealing insights into diabetes and other traits.