Integration of genomic risk scores to improve the prediction of childhood asthma diagnosis
Integration of genomic risk scores to improve the prediction of childhood asthma diagnosis
Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.
Asthma, Childhood, Data integration, Machine learning, Methylation risk score, Polygenic risk score, Prediction
75
Kothalawala, Dilini M.
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Kadalayil, Latha
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Curtin, John A.
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Murray, Clare S.
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Simpson, Angela
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Custovic, Adnan
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Tapper, William J.
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Arshad, S. Hasan
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Rezwan, Faisal I.
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Holloway, John W.
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8 January 2022
Kothalawala, Dilini M.
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Kadalayil, Latha
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Curtin, John A.
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Murray, Clare S.
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Simpson, Angela
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Custovic, Adnan
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Tapper, William J.
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Arshad, S. Hasan
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Rezwan, Faisal I.
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Holloway, John W.
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Kothalawala, Dilini M., Kadalayil, Latha, Curtin, John A., Murray, Clare S., Simpson, Angela, Custovic, Adnan, Tapper, William J., Arshad, S. Hasan, Rezwan, Faisal I. and Holloway, John W.
(2022)
Integration of genomic risk scores to improve the prediction of childhood asthma diagnosis.
Journal of Personalized Medicine, 12 (1), , [75].
(doi:10.3390/jpm12010075).
Abstract
Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted.
Text
jpm-12-00075 (2)
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Accepted/In Press date: 31 December 2021
Published date: 8 January 2022
Additional Information:
This research was funded by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre and a University of Southampton Presidential Research Studentship. Replication analysis in MAAS was supported by the Medical Research Council as part of UNICORN (Unified Cohorts Research Network): Disaggregating asthma MR/S025340/1. Angela Simpson and Clare Murray are supported by the NIHR Manchester Biomedical Research Centre.
The authors would like to acknowledge the help of all the staff at the David Hide Asthma and Allergy Research Centre in undertaking the assessments of the Isle of Wight birth cohort. The authors would also like to thank the IOWBC and MAAS study participants and their parents for their continued support and enthusiasm. Recruitment and initial assessment for the first 4 years of age for the IOWBC was supported by the Isle of Wight Health Authority. The 10-year follow-up of the IOWBC was funded by the National Asthma Campaign, UK (Grant No 364). MAAS was supported by the Asthma UK Grants No 301 (1995–1998), No 362 (1998–2001), No 01/012 (2001–2004), No 04/014 (2004–2007), BMA James Trust (2005) and The JP Moulton Charitable Foundation (2004-current), The North west Lung Centre Charity (1997-current) and the Medical Research Council (MRC) G0601361 (2007–2012), MR/K002449/1 (2013–2014) and MR/L012693/1 (2014–2018). UNICORN (Unified Cohorts Research Network): Disaggregating asthma MR/S025340/1. The authors would also like to acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.
Keywords:
Asthma, Childhood, Data integration, Machine learning, Methylation risk score, Polygenic risk score, Prediction
Identifiers
Local EPrints ID: 454365
URI: http://eprints.soton.ac.uk/id/eprint/454365
ISSN: 2075-4426
PURE UUID: 3f1ccf2d-6d4f-4f2e-828b-730b47f634cb
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Date deposited: 08 Feb 2022 17:35
Last modified: 17 Mar 2024 03:31
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Contributors
Author:
Dilini M. Kothalawala
Author:
John A. Curtin
Author:
Clare S. Murray
Author:
Angela Simpson
Author:
Adnan Custovic
Author:
Faisal I. Rezwan
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