Trajectories of childhood immune development and respiratory health relevant to asthma and allergy
- Baker Heart and Diabetes Institute, Australia
- Imperial College London, United Kingdom
- University of Wisconsin School of Medicine and Public Health, United States
- University of Western Australia, Australia
- University of Manchester, United Kingdom
- The University of Melbourne, Australia
- University of Cambridge, United Kingdom
Abstract
Events in early life contribute to subsequent risk of asthma; however, the causes and trajectories of childhood wheeze are heterogeneous and do not always result in asthma. Similarly, not all atopic individuals develop wheeze, and vice versa. The reasons for these differences are unclear. Using unsupervised model-based cluster analysis, we identified latent clusters within a prospective birth cohort with deep immunological and respiratory phenotyping. We characterised each cluster in terms of immunological profile and disease risk, and replicated our results in external cohorts from the UK and USA. We discovered three distinct trajectories, one of which is a high-risk 'atopic' cluster with increased propensity for allergic diseases throughout childhood. Atopy contributes varyingly to later wheeze depending on cluster membership. Our findings demonstrate the utility of unsupervised analysis in elucidating heterogeneity in asthma pathogenesis and provide a foundation for improving management and prevention of childhood asthma.
Data availability
This study utilises extensive data from human subjects, specifically paediatric cohorts, for which eLife's policies recognise that there can be strong reasons to restrict access. For each of the cohorts involved in our study (CAS, COAST, MAAS), parents were consented on the use of biomedical data for allergy and asthma research, but not for the open sharing of their or their children's data. Studies were run in the late 1990s and early 2000s and we do not have ethics permission to attempt to recontact families to seek consent. Importantly, we note that key data features could risk re-identification of subjects (e.g. demographic data from small communities).However, we have provided public data at the summary level which can be used for subsequent studies, such as replication and meta-analysis. This is standard practice in sensitive data settings, such as genome-wide association studies. These data have been uploaded as Excel spreadsheets to FigShare for ease of data extraction:Supplementary Table 4 https://figshare.com/articles/Supplementary_File_1_1/6934052Supplementary Table 7 https://figshare.com/articles/Supplementary_File_1_2/6934055
Article and author information
Author details
Funding
National Health and Medical Research Council (1049539)
- Michael Inouye
National Health and Medical Research Council (PhD Scholarship)
- Howard HF Tang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- M Dawn Teare, University of Sheffield, United Kingdom
Ethics
Human subjects: Ethics approval and consent requirements for each cohort were met as follows: The CAS study was approved by the ethics committees of the King Edward Memorial and Princess Margaret Hospitals in Western Australia; fully informed parental consent was obtained for all subjects. The COAST study was approved by the Human Subjects Committee of the University of Wisconsin. The MAAS study was approved by a Manchester Local Research Ethics Committee (ERP/94/032; SOU/00/258; 03/SM/400; Study registration ISRCTN72673620); fully informed parental consent was obtained for all subjects across all cohorts.
Version history
- Received: February 12, 2018
- Accepted: October 5, 2018
- Accepted Manuscript published: October 15, 2018 (version 1)
- Version of Record published: November 7, 2018 (version 2)
Copyright
© 2018, Tang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,921
- views
-
- 284
- downloads
-
- 21
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Trajectories of childhood immune development and respiratory health relevant to asthma and allergy
eLife 7:e35856.
https://doi.org/10.7554/eLife.35856
Further reading
-
- Computational and Systems Biology
- Neuroscience
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a ‘synergistic global workspace’, comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain’s default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
-
- Computational and Systems Biology
- Genetics and Genomics
Runs-of-homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for the efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE (runs-of-homozygous diplotype cluster enumerator), to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 single nucleotide polymorphisms (SNPs) and are shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended human leukocyte antigen (HLA) region and autoimmune disorders. We found an association between a diplotype covering the homeostatic iron regulator (HFE) gene and hemochromatosis, even though the well-known causal SNP was not directly genotyped or imputed. Using a genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase in mortality among COVID-19 patients (p-value = 1.82 × 10−11). In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at a population scale.
