Discerning asthma endotypes through comorbidity mapping

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, Yoichiro Kamatani, Masato Akiyama, Koichi Matsuda, Michiaki Kubo, Nancy J. Cox, Carole Ober (), Andrey Rzhetsky () and Julian Solway ()
Additional contact information
Gengjie Jia: University of Chicago
Xue Zhong: Vanderbilt University Medical Center
Hae Kyung Im: University of Chicago
Nathan Schoettler: University of Chicago
Milton Pividori: University of Chicago
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
Yoichiro Kamatani: RIKEN Center for Integrative Medical Sciences
Masato Akiyama: RIKEN Center for Integrative Medical Sciences
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
Julian Solway: University of Chicago

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33628-8

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DOI: 10.1038/s41467-022-33628-8

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