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

Details

Serval ID
serval:BIB_3FF1303159F0
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Assessment and characterization of phenotypic heterogeneity of anxiety disorders across five large cohorts.
Journal
International journal of methods in psychiatric research
Author(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
Publication state
Published
Issued date
12/2016
Peer-reviewed
Oui
Volume
25
Number
4
Pages
255-266
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
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.

Keywords
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
Create date
19/07/2016 8:41
Last modification date
20/08/2019 13:37
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