Complex trait methylation scores in the prediction of major depressive disorder
Miruna C. Barbu,
Carmen Amador,
Alex S.F. Kwong,
Xueyi Shen,
Mark J. Adams,
David M. Howard,
Rosie M. Walker,
Stewart W. Morris,
Josine L. Min,
Chunyu Liu,
Jenny van Dongen,
Mohsen Ghanbari,
Caroline Relton,
David J. Porteous,
Archie Campbell,
Kathryn L. Evans,
Heather C. Whalley,
Andrew M. McIntosh
Affiliations
Miruna C. Barbu
Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, United Kingdom; Corresponding author.
Carmen Amador
MRC Human Genetics Unit, The Institute of Genetics and Cancer, The University of Edinburgh, United Kingdom
Alex S.F. Kwong
Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, United Kingdom
Xueyi Shen
Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, United Kingdom
Mark J. Adams
Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, United Kingdom
David M. Howard
Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, United Kingdom; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
Rosie M. Walker
Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, United Kingdom
Stewart W. Morris
Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, United Kingdom
Josine L. Min
Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, United Kingdom
Chunyu Liu
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; The Framingham Heart Study, Framingham, MA, USA
Jenny van Dongen
Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Mohsen Ghanbari
Department of Epidemiology, Erasmus University Medical Center Rotterdam, the Netherlands
Caroline Relton
Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, United Kingdom
David J. Porteous
Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, United Kingdom
Archie Campbell
Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, United Kingdom
Kathryn L. Evans
Centre for Genomic and Experimental Medicine, The Institute of Genetics and Cancer, The University of Edinburgh, United Kingdom
Heather C. Whalley
Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, United Kingdom
Andrew M. McIntosh
Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, United Kingdom
Summary: Background: DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions. Methods: Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPACadults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432). Findings: Each trait MS was significantly associated with its corresponding phenotype in GS (βrange=0.089–1.457) and in ALSPAC (βrange=0.078–2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (βrange=0.053–0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MSp-value<0.01–0.5; βrange=-0.069–0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes. Interpretation: We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (pnominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (NALSPAC=565; NGS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes. Funding: Wellcome Trust.