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Allergy. 2019 Feb 9. doi: 10.1111/all.13745. [Epub ahead of print]

A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological and environmental factors.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
2
Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Boltzmannstr. 3, 85748, Garching, Germany.
3
Department of Pulmonary and Allergy, Dr. von Hauner Children's Hospital, LMU, Lindwurmstraße 4, 80337, Munich, Germany.
4
Member of German Lung Centre (DZL), CPC, Munich, Germany.
5
University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX, 78229, US.
6
Faculty of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany.

Abstract

BACKGROUND:

Associations between childhood asthma phenotypes and genetic, immunological and environmental factors have been previously established. Yet, strategies to integrate high-dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data and lack of methods to accommodate them.

METHODS:

We assembled questionnaire, diagnostic, genotype, microarray, RT-qPCR, flow cytometry and cytokine data (referred to as data modalities) to use as input factors for a classifier that could distinguish healthy children, mild-to-moderate allergic asthmatics and non-allergic asthmatics. Based on data from 260 German children aged 4-14 from our university outpatient clinic, we built a novel multi-level prediction approach for asthma outcome which could deal with a present complex missing data structure.

RESULTS:

The optimal learning method was boosting based on all data sets, achieving an area-underneath-the-receiver-operating-characteristic curve (AUC) for three classes of phenotypes of 0.81 (95%-confidence interval (CI): 0.65-0.94) using leave-one-out cross-validation. Besides improving the AUC, our integrative multi-level learning approach led to tighter CIs than using smaller complete predictor data sets (AUC=0.82[0.66-0.94] for boosting). The most important variables for classifying childhood asthma phenotypes comprised novel identified genes, namely PKN2 (protein kinase N2), PTK2 (protein tyrosine kinase 2), and ALPP (alkaline phosphatase, placental).

CONCLUSION:

Our combination of several data modalities using a novel strategy improved classification of childhood asthma phenotypes but requires validation in external populations. The generic approach is applicable to other multi-level data-based risk prediction settings, which typically suffer from incomplete data. This article is protected by copyright. All rights reserved.

KEYWORDS:

Childhood asthma; complex study design; immunology; machine learning; risk prediction

PMID:
30737985
DOI:
10.1111/all.13745

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