Assessment and characterization of phenotypic heterogeneity of anxiety disorders across five large cohorts.

Détails

ID Serval
serval:BIB_3FF1303159F0
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Assessment and characterization of phenotypic heterogeneity of anxiety disorders across five large cohorts.
Périodique
International journal of methods in psychiatric research
Auteur(s)
Lee M., Aggen S.H., Otowa T., Castelao E., Preisig M., Grabe H.J., Hartman C.A., Oldehinkel A.J., Middeldorp C.M., Tiemeier H., Hettema J.M.
ISSN
1557-0657 (Electronic)
ISSN-L
1049-8931
Statut éditorial
Publié
Date de publication
12/2016
Peer-reviewed
Oui
Volume
25
Numéro
4
Pages
255-266
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
To achieve sample sizes necessary for effectively conducting genome-wide association studies (GWASs), researchers often combine data from samples possessing multiple potential sources of heterogeneity. This is particularly relevant for psychiatric disorders, where symptom self-report, differing assessment instruments, and diagnostic comorbidity complicates the phenotypes and contribute to difficulties with detecting and replicating genetic association signals. We investigated sources of heterogeneity of anxiety disorders (ADs) across five large cohorts used in a GWAS meta-analysis project using a dimensional structural modeling approach including confirmatory factor analyses (CFAs) and measurement invariance (MI) testing. CFA indicated a single-factor model provided the best fit in each sample with the same pattern of factor loadings. MI testing indicated degrees of failure of metric and scalar invariance which depended on the inclusion of the effects of sex and age in the model. This is the first study to examine the phenotypic structure of psychiatric disorder phenotypes simultaneously across multiple, large cohorts used for GWAS. The analyses provide evidence for higher order invariance but possible break-down at more detailed levels that can be subtly influenced by included covariates, suggesting caution when combining such data. These methods have significance for large-scale collaborative studies that draw on multiple, potentially heterogeneous datasets. Copyright © 2016 John Wiley & Sons, Ltd.

Mots-clé
Adult, Aged, Anxiety Disorders/diagnosis, Cohort Studies, Female, Genome-Wide Association Study/standards, Humans, Male, Meta-Analysis as Topic, Middle Aged, Phenotype, anxiety disorder, factor analysis, measurement invariance
Pubmed
Web of science
Création de la notice
19/07/2016 9:41
Dernière modification de la notice
20/08/2019 14:37
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