Literature DB >> 36169986

Genome-by-Trauma Exposure Interactions in Adults With Depression in the UK Biobank.

Melisa Chuong1,2, Mark J Adams2, Alex S F Kwong2, Chris S Haley1, Carmen Amador1, Andrew M McIntosh2.   

Abstract

Importance: Self-reported trauma exposure has consistently been found to be a risk factor for major depressive disorder (MDD), and several studies have reported interactions with genetic liability. To date, most studies have examined gene-environment interactions with trauma exposure using genome-wide variants (single-nucleotide variations [SNVs]) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance. Objective: To reexamine genome-by-trauma interaction associations using genetic measures using all available genotyped data and thus, maximizing accounted variance. Design, Setting, and Participants: The UK Biobank study was conducted from April 2007 to May 1, 2016 (follow-up mental health questionnaire). The current study used available cross-sectional genomic and trauma exposure data from UK Biobank. Participants who completed the mental health questionnaire and had available genetic, trauma experience, depressive symptoms, and/or neuroticism information were included. Data were analyzed from April 1 to August 30, 2021. Exposures: Trauma and genome-by-trauma exposure interactions. Main Outcomes and Measures: Measures of self-reported depression, neuroticism, and trauma exposure with whole-genome SNV data are available from the UK Biobank study. Here, a mixed-model statistical approach using genetic, trauma exposure, and genome-by-trauma exposure interaction similarity matrices was used to explore sources of variation in depression and neuroticism.
Results: Analyses were conducted on 148 129 participants (mean [SD] age, 56 [7] years) of which 76 995 were female (52.0%). The study approach estimated the heritability (SE) of MDD to be approximately 0.160 (0.016). Subtypes of self-reported trauma exposure (catastrophic, adult, childhood, and full trauma) accounted for a significant proportion of the variance of MDD, with heritability (SE) ranging from 0.056 (0.013) to 0.176 (0.025). The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction revealed estimates (SD) ranging from 0.074 (0.006) to 0.201 (0.009). Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in male participants (0.441 [0.018]) than in female participants (0.086 [0.009]). Conclusions and Relevance: This cross-sectional study used an approach combining all genome-wide SNV data when exploring genome-by-trauma interactions in individuals with MDD; findings suggest that such interactions were associated with depression manifestation. Genome-by-trauma interaction accounts for greater trait variance in male individuals, which points to potential differences in depression etiology between the sexes. The methodology used in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.

Entities:  

Year:  2022        PMID: 36169986      PMCID: PMC9520433          DOI: 10.1001/jamapsychiatry.2022.2983

Source DB:  PubMed          Journal:  JAMA Psychiatry        ISSN: 2168-622X            Impact factor:   25.911


  52 in total

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2.  Linear mixed model for heritability estimation that explicitly addresses environmental variation.

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Journal:  Proc Natl Acad Sci U S A       Date:  2016-07-05       Impact factor: 11.205

3.  Effects of polygenic risk score, childhood trauma and resilience on depressive symptoms in Chinese adolescents in a three-year cohort study.

Authors:  Ning Shao; Yusha Gong; Ximin Wang; Jishan Wei; Junxin Shi; Huisi Ding; Minli Zhang; Chun Kang; Sichao Wang; Lecheng Chen; Yizhen Yu; Juan Han
Journal:  J Affect Disord       Date:  2020-12-31       Impact factor: 4.839

4.  Effect of polygenic risk scores on depression in childhood trauma.

Authors:  Wouter J Peyrot; Yuri Milaneschi; Abdel Abdellaoui; Patrick F Sullivan; Jouke J Hottenga; Dorret I Boomsma; Brenda W J H Penninx
Journal:  Br J Psychiatry       Date:  2014-06-12       Impact factor: 9.319

5.  Meta-analysis of the heritability of human traits based on fifty years of twin studies.

Authors:  Tinca J C Polderman; Beben Benyamin; Christiaan A de Leeuw; Patrick F Sullivan; Arjen van Bochoven; Peter M Visscher; Danielle Posthuma
Journal:  Nat Genet       Date:  2015-05-18       Impact factor: 38.330

6.  Gene-environment interactions in common mental disorders: an update and strategy for a genome-wide search.

Authors:  Rudolf Uher
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2013-12-10       Impact factor: 4.328

7.  Sex differences in the pathways to major depression: a study of opposite-sex twin pairs.

Authors:  Kenneth S Kendler; Charles O Gardner
Journal:  Am J Psychiatry       Date:  2014-04       Impact factor: 18.112

Review 8.  Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.

Authors:  Hannah L Nicholls; Christopher R John; David S Watson; Patricia B Munroe; Michael R Barnes; Claudia P Cabrera
Journal:  Front Genet       Date:  2020-04-15       Impact factor: 4.599

9.  Genome-wide methylation data improves dissection of the effect of smoking on body mass index.

Authors:  Carmen Amador; Yanni Zeng; Michael Barber; Rosie M Walker; Archie Campbell; Andrew M McIntosh; Kathryn L Evans; David J Porteous; Caroline Hayward; James F Wilson; Pau Navarro; Chris S Haley
Journal:  PLoS Genet       Date:  2021-09-09       Impact factor: 5.917

10.  Exploring the genetic heterogeneity in major depression across diagnostic criteria.

Authors:  Bradley S Jermy; Kylie P Glanville; Jonathan R I Coleman; Cathryn M Lewis; Evangelos Vassos
Journal:  Mol Psychiatry       Date:  2021-07-21       Impact factor: 15.992

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