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A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

Author

Listed:
  • Anna Halama

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine)

  • Shaza Zaghlool

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine)

  • Gaurav Thareja

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine)

  • Sara Kader

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine)

  • Wadha Al Muftah

    (Innovation Center
    Weill Cornell Medicine)

  • Marjonneke Mook-Kanamori

    (Weill Cornell Medicine)

  • Hina Sarwath

    (Weill Cornell Medicine-Qatar, Education City)

  • Yasmin Ali Mohamoud

    (Weill Cornell Medicine-Qatar, Education City)

  • Nisha Stephan

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine)

  • Sabine Ameling

    (University Medicine Greifswald
    University Medicine Greifswald)

  • Maja Pucic Baković

    (Genos Glycoscience Research Laboratory)

  • Jan Krumsiek

    (Weill Cornell Medicine
    Weill Cornell Medicine)

  • Cornelia Prehn

    (Helmholtz Zentrum München)

  • Jerzy Adamski

    (Helmholtz Zentrum München, German Research Center for Environmental Health
    National University of Singapore
    University of Ljubljana)

  • Jochen M. Schwenk

    (KTH Royal Institute of Technology)

  • Nele Friedrich

    (University Medicine Greifswald
    University Medicine Greifswald)

  • Uwe Völker

    (University Medicine Greifswald
    University Medicine Greifswald)

  • Manfred Wuhrer

    (Leiden University Medical Center)

  • Gordan Lauc

    (Genos Glycoscience Research Laboratory
    University of Zagreb)

  • S. Hani Najafi-Shoushtari

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine)

  • Joel A. Malek

    (Weill Cornell Medicine
    Weill Cornell Medicine-Qatar, Education City)

  • Johannes Graumann

    (Philipps-Universität Marburg)

  • Dennis Mook-Kanamori

    (Leiden University Medical Center
    Leiden University Medical Center)

  • Frank Schmidt

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine)

  • Karsten Suhre

    (Weill Cornell Medicine-Qatar, Education City
    Weill Cornell Medicine
    Weill Cornell Medicine)

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.

Suggested Citation

  • Anna Halama & Shaza Zaghlool & Gaurav Thareja & Sara Kader & Wadha Al Muftah & Marjonneke Mook-Kanamori & Hina Sarwath & Yasmin Ali Mohamoud & Nisha Stephan & Sabine Ameling & Maja Pucic Baković & Jan, 2024. "A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51134-x
    DOI: 10.1038/s41467-024-51134-x
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