Methylome-wide association study of antidepressant use in Generation Scotland and the Netherlands Twin Register implicates the innate immune system
Year: 2021
Type: review
Study design: Observational Study
Abstract: Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS, N=6,428, EPIC array) and the Netherlands Twin Register (NTR, N=2,449, 450K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found 10 CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders, ATP6V1B2 (β=-0.055, p corrected =0.005). Other top loci were annotated to genes including CASP10, TMBIM1, MAPKAPK3, and HEBP2 , which have previously been implicated in the innate immune response. Next, using penalised regression, we trained a methylation-based score of self-reported antidepressant use in a subset of 3,799 GS:SFHS individuals that predicted antidepressant use in a second subset of GS:SFHS (N=3,360, β=0.377, p=3.12×10 −11 , R 2 =2.12%). In an MWAS analysis of prescribed selective serotonin reuptake inhibitors, we showed convergent findings with those based on self-report. In NTR, we did not find any CpGs significantly associated with antidepressant use. The meta-analysis identified the two CpGs of the ten above that were common to the two arrays used as being significantly associated with antidepressant use, although the effect was in the opposite direction for one of them. Antidepressants were associated with epigenetic alterations in loci previously associated with mental health disorders and the innate immune system. These changes predicted self-reported antidepressant use in a subset of GS:SFHS and identified processes that may be relevant to our mechanistic understanding of clinically relevant antidepressant drug actions and side effects.
Source: Molecular Psychiatry
Authors: Miruna C. Barbu, Floris Huider, Archie Campbell, Carmen Amador, Mark J. Adams, Mary-Ellen Lynall, David M. Howard, Rosie M. Walker, Stewart W. Morris, Jenny Van Dongen, David J. Porteous, Kathryn L. Evans, Edward Bullmore, Gonneke Willemsen, Dorret I. Boomsma, Heather C. Whalley, Andrew M. McIntosh
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,480
Funders: National Alliance for Research on Schizophrenia and Depression, Wellcome Trust, Scottish Government, Royal College of Physicians of Edinburgh, National Institute for Health and Care Research, Scottish Funding Council, Guarantors of Brain, Chief Scientist Office, Scottish Government Health and Social Care Directorate, Medical Research Council
Awards: NIHR Senior Investigator Award, G2437620908, G259633682, Exploiting genomic approaches to identify the environmental basis of depression, NIHR Senior Investigator Award, Exploiting genomic approaches to identify the environmental basis of depression, Using multiple data sources to stratify depression and identify more targeted drug treatments, Stratifying Resilience and Depression Longitudinally (STRADL), Refurbishment of the Herschel Smith building for Clinical Research in Cognitive Neuroscience at Cambridg University, DATAMIND: Data Hub for Mental health INformatics research Development, Quantitative Traits in Health and Disease, Stratifying Resilience and Depression Longitudinally (STRADL), G729635221, G7304791810, G839044655, Identifying and characterising a cellular blood biomarker in treatment-resistant depression, Using multiple data sources to stratify depression and identify more targeted drug treatments, G8724570043
OpenAlex ID: W4200085093
