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Microbiome-wide association studies link dynamic microbial consortia to disease

Author

Listed:
  • Jack A. Gilbert

    (University of Chicago)

  • Robert A. Quinn

    (University of California
    Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California
    Center for Microbiome Innovation, Jacobs School of Engineering, University of California)

  • Justine Debelius

    (University of California, San Diego School of Medicine)

  • Zhenjiang Z. Xu

    (University of California, San Diego School of Medicine)

  • James Morton

    (Jacobs School of Engineering, University of California)

  • Neha Garg

    (University of California
    Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California)

  • Janet K. Jansson

    (Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory)

  • Pieter C. Dorrestein

    (University of California
    Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California
    Center for Microbiome Innovation, Jacobs School of Engineering, University of California
    University of California, San Diego School of Medicine)

  • Rob Knight

    (Center for Microbiome Innovation, Jacobs School of Engineering, University of California
    University of California, San Diego School of Medicine
    Jacobs School of Engineering, University of California)

Abstract

Rapid advances in DNA sequencing, metabolomics, proteomics and computational tools are dramatically increasing access to the microbiome and identification of its links with disease. In particular, time-series studies and multiple molecular perspectives are facilitating microbiome-wide association studies, which are analogous to genome-wide association studies. Early findings point to actionable outcomes of microbiome-wide association studies, although their clinical application has yet to be approved. An appreciation of the complexity of interactions among the microbiome and the host's diet, chemistry and health, as well as determining the frequency of observations that are needed to capture and integrate this dynamic interface, is paramount for developing precision diagnostics and therapies that are based on the microbiome.

Suggested Citation

  • Jack A. Gilbert & Robert A. Quinn & Justine Debelius & Zhenjiang Z. Xu & James Morton & Neha Garg & Janet K. Jansson & Pieter C. Dorrestein & Rob Knight, 2016. "Microbiome-wide association studies link dynamic microbial consortia to disease," Nature, Nature, vol. 535(7610), pages 94-103, July.
    • Handle: RePEc:nat:nature:v:535:y:2016:i:7610:d:10.1038_nature18850
    DOI: 10.1038/nature18850
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    Citations

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    Cited by:

    1. Jiyuan Hu & Chan Wang & Martin J. Blaser & Huilin Li, 2022. "Joint modeling of zero‐inflated longitudinal proportions and time‐to‐event data with application to a gut microbiome study," Biometrics, The International Biometric Society, vol. 78(4), pages 1686-1698, December.
    2. Pamela N Luna & Jonathan M Mansbach & Chad A Shaw, 2020. "A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-17, December.
    3. Zengliang Jiang & Lai-bao Zhuo & Yan He & Yuanqing Fu & Luqi Shen & Fengzhe Xu & Wanglong Gou & Zelei Miao & Menglei Shuai & Yuhui Liang & Congmei Xiao & Xinxiu Liang & Yunyi Tian & Jiali Wang & Jun T, 2022. "The gut microbiota-bile acid axis links the positive association between chronic insomnia and cardiometabolic diseases," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Doris Vandeputte & Lindsey Commer & Raul Y. Tito & Gunter Kathagen & João Sabino & Séverine Vermeire & Karoline Faust & Jeroen Raes, 2021. "Temporal variability in quantitative human gut microbiome profiles and implications for clinical research," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    5. Zheng-Zheng Tang & Guanhua Chen, 2021. "Robust and Powerful Differential Composition Tests for Clustered Microbiome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 200-216, July.
    6. Rajita Menon & Vivek Ramanan & Kirill S Korolev, 2018. "Interactions between species introduce spurious associations in microbiome studies," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-20, January.
    7. Grant D Stentiford & Kallaya Sritunyalucksana & Timothy W Flegel & Bryony A P Williams & Boonsirm Withyachumnarnkul & Orn Itsathitphaisarn & David Bass, 2017. "New Paradigms to Help Solve the Global Aquaculture Disease Crisis," PLOS Pathogens, Public Library of Science, vol. 13(2), pages 1-6, February.

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