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Original Investigation
February 12, 2020

Association of a Reproducible Epigenetic Risk Profile for Schizophrenia With Brain Methylation and Function

Author Affiliations
  • 1Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
  • 2School of Medicine & University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany
  • 3Life & Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
JAMA Psychiatry. Published online February 12, 2020. doi:10.1001/jamapsychiatry.2019.4792
Key Points

Question  Can a blood marker of epigenetic risk for schizophrenia be derived that is specific for the disease and predicts epigenetic changes in brain and disease-associated brain function?

Findings  In this case-control study, machine learning was used to identify a reproducible schizophrenia blood DNA-methylation signature that was associated with functional dorsolateral prefrontal cortex hippocampal connectivity, mapped to methylation differences found in dorsolateral prefrontal cortex hippocampal connectivity postmortem samples, and indexed biological pathways associated with synaptic function. No interactions with polygenic schizophrenia risk were found.

Meaning  These findings support the presence of a systemic methylation profile in schizophrenia that is associated with established intermediate functional phenotypes as well as with epigenetic signatures in brain and should thus be useful to capture the biological effects of gene-environment interactions.

Abstract

Importance  Schizophrenia is a severe mental disorder in which epigenetic mechanisms may contribute to illness risk. Epigenetic profiles can be derived from blood cells, but to our knowledge, it is unknown whether these predict established brain alterations associated with schizophrenia.

Objective  To identify an epigenetic signature (quantified as polymethylation score [PMS]) of schizophrenia using machine learning applied to genome-wide blood DNA-methylation data; evaluate whether differences in blood-derived PMS are mirrored in data from postmortem brain samples; test whether the PMS is associated with alterations of dorsolateral prefrontal cortex hippocampal (DLPFC-HC) connectivity during working memory in healthy controls (HC); explore the association between interactions between polygenic and epigenetic risk with DLPFC-HC connectivity; and test the specificity of the signature compared with other serious psychiatric disorders.

Design, Setting, and Participants  In this case-control study conducted from 2008 to 2018 in sites in Germany, the United Kingdom, the United States, and Australia, blood DNA-methylation data from 2230 whole-blood samples from 6 independent cohorts comprising HC (1238 [55.5%]) and participants with schizophrenia (803 [36.0%]), bipolar disorder (39 [1.7%]), major depressive disorder 35 [1.6%]), and autism (27 [1.2%]), and first-degree relatives of all patient groups (88 [3.9%]) were analyzed. DNA-methylation data were further explored from 244 postmortem DLPFC samples from 136 HC and 108 patients with schizophrenia. Neuroimaging and genome-wide association data were available for 393 HC. The latter data was used to calculate a polygenic risk score (PRS) for schizophrenia. The data were analyzed in 2019.

Main Outcomes and Measures  The accuracy of schizophrenia control classification based on machine learning using epigenetic data; association of schizophrenia PMS scores with DLPFC-HC connectivity; and association of the interaction between PRS and PMS with DLPFC-HC connectivity.

Results  This study included 7488 participants (4395 men [58.7%]), of whom 3158 (2230 men [70.6%]) received a diagnosis of schizophrenia. The PMS signature was associated with schizophrenia across 3 independent data sets (area under the curve [AUC] from 0.69 to 0.78; P value from 0.049 to 1.24 × 10−7) and data from postmortem DLPFC samples (AUC = 0.63; P = 1.42 × 10−4), but not with major depressive disorder (AUC = 0.51; P = .16), autism (AUC = 0.53; P = .66), or bipolar disorder (AUC = 0.58; P = .21). Pathways contributing most to the classification included synaptic processes. Healthy controls with schizophrenia-like PMS showed significantly altered DLPFC-HC connectivity (validation methylation/magnetic resonance imaging, t < −3.81; P for familywise error, <.04; validation magnetic resonance imaging, t < −3.54; P for familywise error, <.02), mirroring the lack of functional decoupling in schizophrenia. There was no significant association of the interaction between PMS and PRS with DLPFC-HC connectivity (P > .19).

Conclusions and Relevance  We identified a reproducible blood DNA-methylation signature specific for schizophrenia that was correlated with altered functional DLPFC-HC coupling during working memory and mapped to methylation differences found in DLPFC postmortem samples. This indicates a possible epigenetic contribution to a schizophrenia intermediate phenotype and suggests that PMS could be of interest to be studied in the context of multimodal biomarkers for disease stratification and treatment personalization.

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