Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis.
asthma
childhood
data integration
machine learning
methylation risk score
polygenic risk score
prediction
Journal
Journal of personalized medicine
ISSN: 2075-4426
Titre abrégé: J Pers Med
Pays: Switzerland
ID NLM: 101602269
Informations de publication
Date de publication:
08 Jan 2022
08 Jan 2022
Historique:
received:
09
11
2021
revised:
18
12
2021
accepted:
31
12
2021
entrez:
21
1
2022
pubmed:
22
1
2022
medline:
22
1
2022
Statut:
epublish
Résumé
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 (
Identifiants
pubmed: 35055391
pii: jpm12010075
doi: 10.3390/jpm12010075
pmc: PMC8777841
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : University of Southampton
ID : Presidential Research Scholarship
Organisme : Medical Research Council
ID : G0601361
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S025340/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : UNICORN (Unified Cohorts Research Network): Disaggregating asthma (MR/S025340/1)
Pays : United Kingdom
Organisme : NIHR Southampton Biomedical Research Centre
ID : NA
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