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GWAS Study

Using phenotype risk scores to enhance gene discovery for generalized anxiety disorder and posttraumatic stress disorder.

Wendt FR, Pathak GA, Deak JD et al.

35181757 PubMed ID
GWAS Study Type
489579 Participants
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Chapter I

Publication Details

Comprehensive information about this research publication

Authors

WF
Wendt FR
PG
Pathak GA
DJ
Deak JD
DA
De Angelis F
KD
Koller D
CB
Cabrera-Mendoza B
LD
Lebovitch DS
LD
Levey DF
SM
Stein MB
KH
Kranzler HR
KK
Koenen KC
GJ
Gelernter J
HL
Huckins LM
PR
Polimanti R
Chapter II

Abstract

Summary of the research findings

UK Biobank (UKB) is a key contributor in mental health genome-wide association studies (GWAS) but only ~31% of participants completed the Mental Health Questionnaire ("MHQ responders"). We predicted generalized anxiety disorder (GAD), posttraumatic stress disorder (PTSD), and major depression symptoms using elastic net regression in the ~69% of UKB participants lacking MHQ data ("MHQ non-responders"; NTraining = 50%; NTest = 50%), maximizing the informative sample for these traits. MHQ responders were more likely to be female, from higher socioeconomic positions, and less anxious than non-responders. Genetic correlation of GAD and PTSD between MHQ responders and non-responders ranged from 0.636 to 1.08; both were predicted by polygenic scores generated from independent cohorts. In meta-analyses of GAD (N = 489,579) and PTSD (N = 497,803), we discovered many novel genomic risk loci (13 for GAD and 40 for PTSD). Transcriptomic analyses converged on altered regulation of prenatal dorsolateral prefrontal cortex in these disorders. Our results provide one roadmap by which sample size and statistical power may be improved for gene discovery of incompletely ascertained traits in the UKB and other biobanks with limited mental health assessment.

489,579 European ancestry individuals

Chapter III

Study Statistics

Key metrics and study information

489579
Total Participants
GWAS
Study Type
No
Replicated
European
Ancestry
U.K.
Recruitment Country
Chapter IV

AI-Generated Summary

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