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Discerning asthma endotypes through comorbidity mapping

Author

Listed:
  • Gengjie Jia

    (University of Chicago
    University of Chicago
    Chinese Academy of Agricultural Sciences)

  • Xue Zhong

    (Vanderbilt University Medical Center)

  • Hae Kyung Im

    (University of Chicago
    University of Chicago)

  • Nathan Schoettler

    (University of Chicago)

  • Milton Pividori

    (University of Chicago
    University of Pennsylvania)

  • D. Kyle Hogarth

    (University of Chicago)

  • Anne I. Sperling

    (University of Chicago)

  • Steven R. White

    (University of Chicago)

  • Edward T. Naureckas

    (University of Chicago)

  • Christopher S. Lyttle

    (University of Chicago)

  • Chikashi Terao

    (RIKEN Center for Integrative Medical Sciences
    Clinical Research Center, Shizuoka General Hospital
    University of Shizuoka)

  • Yoichiro Kamatani

    (RIKEN Center for Integrative Medical Sciences
    The University of Tokyo)

  • Masato Akiyama

    (RIKEN Center for Integrative Medical Sciences
    Kyushu University)

  • Koichi Matsuda

    (The University of Tokyo)

  • Michiaki Kubo

    (RIKEN Center for Integrative Medical Sciences)

  • Nancy J. Cox

    (Vanderbilt University Medical Center)

  • Carole Ober

    (University of Chicago)

  • Andrey Rzhetsky

    (University of Chicago
    University of Chicago
    University of Chicago
    University of Chicago)

  • Julian Solway

    (University of Chicago)

Abstract

Asthma is a heterogeneous, complex syndrome, and identifying asthma endotypes has been challenging. We hypothesize that distinct endotypes of asthma arise in disparate genetic variation and life-time environmental exposure backgrounds, and that disease comorbidity patterns serve as a surrogate for such genetic and exposure variations. Here, we computationally discover 22 distinct comorbid disease patterns among individuals with asthma (asthma comorbidity subgroups) using diagnosis records for >151 M US residents, and re-identify 11 of the 22 subgroups in the much smaller UK Biobank. GWASs to discern asthma risk loci for individuals within each subgroup and in all subgroups combined reveal 109 independent risk loci, of which 52 are replicated in multi-ancestry meta-analysis across different ethnicity subsamples in UK Biobank, US BioVU, and BioBank Japan. Fourteen loci confer asthma risk in multiple subgroups and in all subgroups combined. Importantly, another six loci confer asthma risk in only one subgroup. The strength of association between asthma and each of 44 health-related phenotypes also varies dramatically across subgroups. This work reveals subpopulations of asthma patients distinguished by comorbidity patterns, asthma risk loci, gene expression, and health-related phenotypes, and so reveals different asthma endotypes.

Suggested Citation

  • Gengjie Jia & Xue Zhong & Hae Kyung Im & Nathan Schoettler & Milton Pividori & D. Kyle Hogarth & Anne I. Sperling & Steven R. White & Edward T. Naureckas & Christopher S. Lyttle & Chikashi Terao & Yoi, 2022. "Discerning asthma endotypes through comorbidity mapping," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33628-8
    DOI: 10.1038/s41467-022-33628-8
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    References listed on IDEAS

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    2. Samatar, Elmi Hassan, 2023. "Assessing the determinants of agricultural productivity in Somalia: An application of an ARDL model," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society (AESS), vol. 13(03), January.

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