TY - JOUR
T1 - Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank
AU - Gillett, Alexandra C.
AU - Jermy, Bradley S.
AU - Lee, Sang Hong
AU - Pain, Oliver
AU - Howard, David M.
AU - Hagenaars, Saskia P.
AU - Hanscombe, Ken B.
AU - Coleman, Jonathan R.I.
AU - Lewis, Cathryn M.
N1 - Funding Information:
This paper represents independent research funded by the UK Medical Research Council (MR/N015746/1), and the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The authors acknowledge use of the research computing facility at King's College London, Rosalind ( https://rosalind.kcl.ac.uk ), which is delivered in partnership with the NIHR Maudsley BRC, and part‐funded by capital equipment grants from the Maudsley Charity (award 980) and Guy's & St. Thomas' Charity (TR130505). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Saskia P. Hagenaars was supported by the Medical Research Council (MR/S0151132). David M. Howard is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z) and a 2018 NARSAD Young Investigator Grant from the Brain & Behaviour Research Foundation (Ref: 27404). This study was conducted under UK Biobank application 18177.
Funding Information:
This paper represents independent research funded by the UK Medical Research Council (MR/N015746/1), and the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The authors acknowledge use of the research computing facility at King's College London, Rosalind (https://rosalind.kcl.ac.uk), which is delivered in partnership with the NIHR Maudsley BRC, and part-funded by capital equipment grants from the Maudsley Charity (award 980) and Guy's & St. Thomas' Charity (TR130505). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Saskia P. Hagenaars was supported by the Medical Research Council (MR/S0151132). David M. Howard is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z) and a 2018 NARSAD Young Investigator Grant from the Brain & Behaviour Research Foundation (Ref: 27404). This study was conducted under UK Biobank application 18177.
Publisher Copyright:
© 2022 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene–environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294–91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits—12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic–covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual–covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs—one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic–covariate and residual–covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual–covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic–covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic–environment interactions.
AB - Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene–environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294–91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits—12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic–covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual–covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs—one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic–covariate and residual–covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual–covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic–covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic–environment interactions.
KW - depressive symptoms
KW - genotype–environment interaction
KW - multivariate reaction norm model
KW - residual–environment interaction
UR - http://www.scopus.com/inward/record.url?scp=85128258919&partnerID=8YFLogxK
U2 - 10.1002/gepi.22449
DO - 10.1002/gepi.22449
M3 - Article
AN - SCOPUS:85128258919
SN - 0741-0395
VL - 46
SP - 219
EP - 233
JO - Genetic Epidemiology
JF - Genetic Epidemiology
IS - 5-6
ER -